Method for index sorting unique phenotypes and systems for same

ABSTRACT

Aspects of the present disclosure include methods for flow cytometrically sorting a sample with particles, such as cells, based on order of identification. Methods according to certain embodiments include introducing the sample into a flow cytometer; flowing the introduced sample in a flow stream; irradiating the sample in the flow stream with a light source; detecting light from cells in the sample flowing in the flow stream; identifying phenotypes of cells in the sample flowing in the flow stream based on one or more data signals generated from the detected light; and dynamically sorting into partitions cells of the sample that have a phenotype of a collection of predetermined phenotypes based on order of identification. Systems for practicing the subject methods are also provided. Non-transitory computer readable storage mediums are also described.

CROSS-REFERENCE TO RELATED APPLICATION

Pursuant to 35 U.S.C. § 119(e), this application claims priority to thefiling date of U.S. Provisional Patent Application Ser. No. 63/016,702filed Apr. 28, 2020; the disclosure of which application is hereinincorporated by reference.

INTRODUCTION

Flow-type particle sorting systems, such as sorting flow cytometers, areused to sort particles in a fluid sample based on at least one measuredcharacteristic of the particles. In a flow-type particle sorting system,particles, such as molecules, analyte-bound beads, or individual cells,in a fluid suspension are passed in a stream by a detection region inwhich a sensor detects particles contained in the stream of the type tobe sorted. The sensor, upon detecting a particle of the type to besorted, triggers a sorting mechanism that selectively isolates theparticle of interest. Sorted particles of interest are isolated intopartitions, such as, for example, sample containers, test tubes or wellsof a multi-well plate.

Particle sensing typically is carried out by passing the fluid stream bya detection region in which the particles are exposed to irradiatinglight, from one or more lasers, and the light scattering andfluorescence properties of the particles are measured. Particles orcomponents thereof can be labeled with fluorescent dyes to facilitatedetection, and a multiplicity of different particles or components maybe simultaneously detected by using spectrally distinct fluorescent dyesto label the different particles or components. Detection is carried outusing one or more photosensors to facilitate the independent measurementof the fluorescence of each distinct fluorescent dye.

To sort particles in the sample, a drop charging mechanism chargesdroplets of the flow stream containing a particle type to be sorted withan electrical charge at the break-off point of the flow stream. Dropletsare passed through an electrostatic field and are deflected based onpolarity and magnitude of charge on the droplet into one or morepartitions, such as sample collection containers. Uncharged droplets arenot deflected by the electrostatic field.

SUMMARY

Aspects of the present disclosure include methods for flowcytometrically sorting a sample with particles, such as cells, based onorder of identification. Methods according to certain embodimentsinclude introducing the sample into a flow cytometer, flowing theintroduced sample in a flow stream, irradiating the sample in the flowstream with a light source, detecting light from cells in the sampleflowing in the flow stream, identifying phenotypes of cells in thesample flowing in the flow stream based on one or more data signalsgenerated from the detected light, and dynamically sorting intopartitions cells of the sample that have a phenotype of a collection ofpredetermined phenotypes based on order of identification. Systems forpracticing the subject methods are also provided. Non-transitorycomputer readable storage mediums are also described.

In some embodiments, a first phenotype is excluded from the collectionof phenotypes after sorting a predetermined number of cells that havethe first phenotype. In other embodiments, a predetermined number ofcells are sorted into a partition. In each instance, the predeterminednumber of cells may be, for example, one or two or ten or 100 or more.

In some embodiments, the partitions comprise wells. For instance, thewells may be wells of a multi-well plate. In some embodiments, themulti-well plate is advanced to a second well after sorting apredetermined number of cells into a first well. The predeterminednumber of cells may be, for instance, one or two or ten or 100 or more.

In some embodiments, phenotypes of cells sorted into partitions arerecorded. Phenotypes of cells sorted into partitions may be recorded by,for example, adding representations of cell phenotypes to a list, aprobabilistic data structure, an associative array or a truth table. Insome instances, the record of previously sorted phenotypes is queriedprior to sorting a cell. In embodiments, the list of phenotypes or theprobabilistic data structure or the associative array or the truth tablemay be queried to determine whether or not a first cell has already beensorted, wherein the first cell has a phenotype also exhibited by asecond cell. In some instances, the probabilistic data structure may bea Bloom filter. In these instances, hash functions used in connectionwith the Bloom filter are selected to reduce collisions in the Bloomfilter based on an analysis of the sample. In some embodiments, theassociative array is a content addressable memory.

In some embodiments, information identifying a partition and a phenotypeof a cell sorted into the partition is recorded. For instance, a listidentifying partitions and phenotypes of cells sorted into thepartitions may be maintained. In some embodiments, an estimate, based ona subset of cells in the sample, is made of a number of phenotypesexhibited by cells in the sample.

In certain instances, methods comprise deflecting cells into a firstpartition and a second partition. In these embodiments, the cells in thesample may be divided into a first group that exhibits a firstcollection of phenotypes, comprising a predetermined number ofphenotypes, and a second group that does not exhibit the firstcollection of phenotypes. In such embodiments, the first group may besorted into one of the first partition or the second partition, based onorder of identification. In some instances, the first collection ofphenotypes used to divide cells into a first group and a second groupare determined so that the first group and the second group comprisesubstantially the same number of cells. In these embodiments, the methodmay further comprise dynamically updating the first collection ofphenotypes based on sorted cells in the sample so that the first groupand the second group comprise substantially the same number of cells.

In embodiments, cell phenotypes are identified based on one or morevalues of parameters that are determined from the data signals generatedfrom the detected light. In instances, certain values of a parameter ofa cell indicate an indeterminate state of whether the cell has aphenotype. In such embodiments, a cell may not be sorted when itexhibits an indeterminate state of whether the cell has a phenotype.

In some embodiments, the method includes estimating an amount offluorescent spillover measured with respect to a single data signalbased on a predicted variance-covariance matrix of unmixed data for asorted cell. In these embodiments, the method may include estimating acovariance between two fluorophores based on the predictedvariance-covariance matrix of unmixed data for the sorted cell. In theseembodiments, thresholds may be defined for expression for a fluorophorebased on the estimated covariance between fluorophores. In suchembodiments, the method may include defining a measure of uncertaintywith respect to the phenotype exhibited by a sorted cell based on theestimated covariance between fluorophores.

In certain instances, samples of interest include a plurality offluorophores where the fluorescence spectra of each fluorophore overlapwith the fluorescence spectra of at least one other fluorophore in thesample. For example, the fluorescence spectra of each fluorophore mayoverlap with the fluorescence spectra of at least one other fluorophorein the sample by 10 nm or more, such as 25 nm or more and including by50 nm or more. In some instances, the fluorescence spectra of one ormore fluorophores in the sample overlaps with the fluorescence spectraof two different fluorophores in the sample, such as by 10 nm or more,such as by 25 nm or more and including by 50 nm or more. In otherembodiments, samples of interest include a plurality of fluorophoreshaving non-overlapping fluorescence spectra. In these embodiments, thefluorescence spectra of each fluorophore is adjacent to at least oneother fluorophore within 10 nm or less, such as 9 nm or less, such as 8nm or less, such as 7 nm or less, such as 6 nm or less, such as 5 nm orless, such as 4 nm or less, such as 3 nm or less, such as 2 nm or lessand including 1 nm or less.

In embodiments, the collection of predetermined phenotypes comprises acell type with one or more cell subtypes. For instance, the cell typemay be T cell. In such embodiments, the cell subtypes may include CD4+ Tcells or CD8+ T cells.

Aspects of the present disclosure also include systems having a lightdetection system for characterizing particles of a sample in a flowstream (e.g., cells in a biological sample). Systems according tocertain embodiments include a light source configured to irradiate thesample comprising cells flowing in a flow stream, a light detectionsystem comprising a photodetector for detecting light from the cells inthe sample and generating a plurality of data signals from the detectedlight, a cell sorter configured to receive the sample comprising cellsflowing in the flow stream, a plurality of partitions configured toreceive cells from the sample sorted by the cell sorter, and a processorcomprising memory operably coupled to the processor, wherein the memorycomprises instructions stored thereon, which, when executed by theprocessor, cause the processor to: identify phenotypes of cells in thesample based on one or more of the data signals generated from thedetected light and instruct the cell sorter to dynamically sort into thepartitions cells of the sample that have a phenotype of a collection ofpredetermined phenotypes based on order of identification.

In embodiments, the memory includes instructions stored thereon whichwhen executed by the processor cause the processor to instruct the cellsorter to stop sorting a first phenotype from the collection ofphenotypes after a predetermined number of cells that have the firstphenotype are sorted. In other embodiments, the memory includesinstructions stored thereon which when executed by the processor causethe processor to instruct the cell sorter to dynamically sort apredetermined number of cells into a partition. The predetermined numberof cells may be, for instance, one, two, ten or 100 or more.

In instances, the partitions may comprise wells. In some cases, thewells are wells of a multi-well plate. In some embodiments, systemscomprise a translatable support stage configured to move the multi-wellplate, wherein the memory comprises instructions stored thereon, which,when executed by the processor, cause the processor to instruct thesupport stage to move the multi-well plate to a second well aftersorting a predetermined number of cells into a first well.

In some embodiments, the memory comprises instructions stored thereon,which, when executed by the processor, cause the processor to store thephenotypes of cells sorted into partitions. In these embodiments, thememory may comprise instructions stored thereon, which, when executed bythe processor, cause the processor to store the phenotypes of cellssorted into partitions by adding representations of phenotypes of sortedcells to a list of phenotypes, a probabilistic data structure, anassociative array or a truth table. In some embodiments, the memorycomprises instructions stored thereon, which, when executed by theprocessor, cause the processor to query the list of phenotypes, theprobabilistic data structure, the associative array or the truth table,as applicable, to determine whether or not a first cell has already beensorted, wherein the first cell has a phenotype also exhibited by asecond cell. In some instances, the probabilistic data structure is aBloom filter. In some instances, the associative array is a contentaddressable memory.

In some embodiments, the memory comprises instructions stored thereon,which, when executed by the processor, cause the processor to storeinformation identifying a partition and a phenotype of a cell sortedinto the partition. In these embodiments, the memory may compriseinstructions stored thereon, which, when executed by the processor,cause the processor to maintain a list identifying partitions andphenotypes of cells sorted into the partitions.

In some embodiments, the memory comprises instructions stored thereon,which, when executed by the processor, cause the processor to instructthe cell sorter to: dynamically sort into a first partition cells of thesample that exhibit a first collection of phenotypes, comprising one ormore phenotypes, based on order of identification, and dynamically sortinto a second partition cells of the sample that do not exhibit thefirst collection of phenotypes. In these embodiments, the memory maycomprise instructions stored thereon, which, when executed by theprocessor, cause the processor to dynamically update the firstcollection of phenotypes based on sorted cells in the sample so thatcells sorted into the first partition and cells sorted into the secondpartition comprise substantially the same number of cells.

In some embodiments, the memory comprises instructions stored thereon,which, when executed by the processor, cause the processor to not sort acell exhibiting an indeterminate phenotype.

In some embodiments, the memory comprises instructions stored thereon,which, when executed by the processor, cause the processor to calculatean estimate of an amount of fluorescent spillover measured with respectto a single data signal based on a predicted variance-covariance matrixof unmixed data for a sorted cell. In such embodiments, the memory maycomprise instructions stored thereon, which, when executed by theprocessor, cause the processor to calculate an estimate of a covariancebetween two fluorophores based on the predicted variance-covariancematrix of unmixed data for the sorted cell. In these embodiments, thememory may comprise instructions stored thereon, which, when executed bythe processor, cause the processor to calculate thresholds forexpression for a fluorophore based on the estimated covariance betweenfluorophores. In such embodiments, the memory may comprise instructionsstored thereon, which, when executed by the processor, cause theprocessor to calculate a measure of uncertainty with respect to thephenotype exhibited by a sorted cell based on the estimated covariancebetween fluorophores.

In some embodiments, systems of interest may include one or more sortdecision modules configured to generate a sorting decision for theparticle based on the classification of the particle. In embodiments,systems further include a cell sorter (e.g., having a droplet deflector)for sorting the particles, such as cells, from the flow stream based onthe sort decision generated by the sort decision module.

Aspects of the present disclosure also include a non-transitory computerreadable storage medium for sorting a sample with particles, such ascells, based on order of identification. Non-transitory computerreadable storage mediums according to certain embodiments includeinstructions stored thereon having algorithm for identifying cellphenotypes based on one or more data signals generated from lightdetected from cells of the sample and algorithm for instructing a cellsorter to dynamically sort into partitions cells that have a phenotypeof a collection of predetermined phenotypes based on order ofidentification. The non-transitory computer readable storage medium mayalso include algorithm for instructing the cell sorter to stop sorting afirst phenotype from the collection of phenotypes after a predeterminednumber of cells that have the first phenotype are sorted. Thenon-transitory computer readable storage medium may also includealgorithm for instructing the cell sorter to dynamically sort apredetermined number of cells into a partition. The non-transitorycomputer readable storage medium may also include algorithm forinstructing a support stage to move a multi-well plate to a second wellafter a predetermined number of cells are sorted into a first well. Inthese embodiments, the predetermined number may be one.

In some embodiments, the non-transitory computer readable storage mediummay also include algorithm for recording the phenotypes of cells sortedinto partitions. In such embodiments, the computer readable storagemedium may include algorithm for recording the phenotypes of cellssorted into partitions by adding representations of phenotypes of sortedcells to, for instance, a list of phenotypes, a probabilistic datastructure, an associative array or a truth table.

In these embodiments, the computer readable storage medium may includealgorithm for querying, as applicable, the list of phenotypes, theprobabilistic data structure, the associative array or the truth tableto determine whether or not a first cell has already been sorted,wherein the first cell has a phenotype also exhibited by a second cell.In some cases, the probabilistic data structure is a Bloom filter. Insome cases, the associative array is a content addressable memory.

In some embodiments, the non-transitory computer readable storage mediummay also include algorithm for recording information identifying apartition and a phenotype of a cell sorted into the partition. In theseembodiments, the non-transitory computer readable storage medium mayinclude algorithm for maintaining a list identifying partitions andphenotypes of cells sorted into the partitions.

In some embodiments, the non-transitory computer readable storage mediummay also include algorithm for instructing the cell sorter to:dynamically sort into a first partition cells of the sample that exhibita first collection of phenotypes, comprising one or more phenotypes,based on order of identification, and dynamically sort into a secondpartition cells of the sample that do not exhibit the first collectionof phenotypes. In these instances, the non-transitory computer readablestorage medium may include algorithm for dynamically updating the firstcollection of phenotypes based on sorted cells in the sample so thatcells sorted into the first partition and cells sorted into the secondpartition comprise substantially the same number of cells. In someembodiments, the non-transitory computer readable storage medium mayalso include algorithm for not sorting a cell exhibiting anindeterminate phenotype.

In some embodiments, the non-transitory computer readable storage mediummay also include algorithm for calculating an estimate of an amount offluorescent spillover measured with respect to a single data signalbased on a predicted variance-covariance matrix of unmixed data for asorted cell. In these embodiments, the computer readable storage mediummay also include algorithm for calculating an estimate of a covariancebetween two fluorophores based on the predicted variance-covariancematrix of unmixed data for the sorted cell. In these embodiments, thecomputer readable storage medium may also include algorithm forcalculating thresholds for expression for a fluorophore based on theestimated covariance between fluorophores. In these embodiments, thecomputer readable storage medium may also include algorithm forcalculating a measure of uncertainty with respect to the phenotypeexhibited by a sorted cell based on the estimated covariance betweenfluorophores.

BRIEF DESCRIPTION OF THE FIGURES

The invention may be best understood from the following detaileddescription when read in conjunction with the accompanying drawings.Included in the drawings are the following figures:

FIG. 1 depicts a functional block diagram for one example of a sortingcontrol system according to certain embodiments.

FIG. 2A depicts a schematic drawing of a particle sorter systemaccording to certain embodiments.

FIG. 2B depicts a schematic drawing of a particle sorter systemaccording to certain embodiments.

FIG. 3 depicts a functional block diagram of a particle analysis systemfor computational based sample analysis and particle characterizationaccording to certain embodiments.

FIG. 4 depicts a flow cytometer according to certain embodiments.

FIG. 5 depicts resulting partitions from sorting a sample according tocertain embodiments of the present disclosure.

FIG. 6 depicts an illustration of phenotype identificationclassifications, including an indeterminate phenotype classification.

FIG. 7 depicts a flow diagram for sorting a sample according to certainembodiments of the present disclosure.

FIG. 8 depicts a block diagram of a computing system according tocertain embodiments.

DETAILED DESCRIPTION

Aspects of the present disclosure include methods for flowcytometrically sorting a sample with particles, such as cells, based onorder of identification. Methods according to certain embodimentsinclude introducing the sample into a flow cytometer, flowing theintroduced sample in a flow stream, irradiating the sample in the flowstream with a light source, detecting light from cells in the sampleflowing in the flow stream, identifying phenotypes of cells in thesample flowing in the flow stream based on one or more data signalsgenerated from the detected light, and dynamically sorting intopartitions cells of the sample that have a phenotype of a collection ofpredetermined phenotypes based on order of identification. Systems forpracticing the subject methods are also provided. Non-transitorycomputer readable storage mediums are also described.

Before the present invention is described in greater detail, it is to beunderstood that this invention is not limited to particular embodimentsdescribed, as such may, of course, vary. It is also to be understoodthat the terminology used herein is for the purpose of describingparticular embodiments only, and is not intended to be limiting, sincethe scope of the present invention will be limited only by the appendedclaims.

Where a range of values is provided, it is understood that eachintervening value, to the tenth of the unit of the lower limit unlessthe context clearly dictates otherwise, between the upper and lowerlimit of that range and any other stated or intervening value in thatstated range, is encompassed within the invention.

The upper and lower limits of these smaller ranges may independently beincluded in the smaller ranges and are also encompassed within theinvention, subject to any specifically excluded limit in the statedrange. Where the stated range includes one or both of the limits, rangesexcluding either or both of those included limits are also included inthe invention.

Certain ranges are presented herein with numerical values being precededby the term “about.” The term “about” is used herein to provide literalsupport for the exact number that it precedes, as well as a number thatis near to or approximately the number that the term precedes. Indetermining whether a number is near to or approximately a specificallyrecited number, the near or approximating unrecited number may be anumber which, in the context in which it is presented, provides thesubstantial equivalent of the specifically recited number.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Although any methods andmaterials similar or equivalent to those described herein can also beused in the practice or testing of the present invention, representativeillustrative methods and materials are now described.

All publications and patents cited in this specification are hereinincorporated by reference as if each individual publication or patentwere specifically and individually indicated to be incorporated byreference and are incorporated herein by reference to disclose anddescribe the methods and/or materials in connection with which thepublications are cited. The citation of any publication is for itsdisclosure prior to the filing date and should not be construed as anadmission that the present invention is not entitled to antedate suchpublication by virtue of prior invention. Further, the dates ofpublication provided may be different from the actual publication dateswhich may need to be independently confirmed.

It is noted that, as used herein and in the appended claims, thesingular forms “a”, “an”, and “the” include plural referents unless thecontext clearly dictates otherwise. It is further noted that the claimsmay be drafted to exclude any optional element. As such, this statementis intended to serve as antecedent basis for use of such exclusiveterminology as “solely,” “only” and the like in connection with therecitation of claim elements, or use of a “negative” limitation.

As will be apparent to those of skill in the art upon reading thisdisclosure, each of the individual embodiments described and illustratedherein has discrete components and features which may be readilyseparated from or combined with the features of any of the other severalembodiments without departing from the scope or spirit of the presentinvention. Any recited method can be carried out in the order of eventsrecited or in any other order which is logically possible.

While the apparatus and method has or will be described for the sake ofgrammatical fluidity with functional explanations, it is to be expresslyunderstood that the claims, unless expressly formulated under 35 U.S.C.§ 112, are not to be construed as necessarily limited in any way by theconstruction of “means” or “steps” limitations, but are to be accordedthe full scope of the meaning and equivalents of the definition providedby the claims under the judicial doctrine of equivalents, and in thecase where the claims are expressly formulated under 35 U.S.C. § 112 areto be accorded full statutory equivalents under 35 U.S.C. § 112.

As summarized above, the present disclosure provides methods for flowcytometrically sorting a sample with particles, such as cells, based onorder of identification. In further describing embodiments of thedisclosure, methods for limiting the number of cells sorted that have aparticular phenotype, limiting the number of cells sorted into eachpartition, recording cell phenotypes that have been sorted, deflectingcells into two wells, and adjusting for spectral spillover are firstdescribed in greater detail. Next, systems to practice the subjectmethods are described. Non-transitory computer readable storage mediumsare also described.

Methods for Flow Cytometrically Sorting a Sample with Particles, Such asCells, Based on Order of Identification

Aspects of the present disclosure include methods for flowcytometrically sorting a sample with particles, such as cells, based onorder of identification. In particular, the present disclosure includesmethods for dynamically sorting into partitions particles, such ascells, of the sample that have a phenotype of a collection ofpredetermined phenotypes based on order of identification. By“dynamically sorting based on order of identification” it is meantsorting particles, such as cells, of interest in the order in which suchcells appear in, for example, a flow stream. That is, for example,particles of interest are sorted as and when they are identified in aflow stream during sorting. As described in greater detail herein, thesubject methods according to certain embodiments provide for excluding afirst phenotype from the collection of phenotypes after sorting apredetermined number of cells that have the first phenotype. In otherembodiments, the subject methods according to certain embodimentsprovide for sorting a predetermined number of cells into a partition. Insome embodiments, the subject methods provide for recording thephenotypes of cells sorted into partitions. Sorting particles, such ascells, according to the subject methods results in increased sortingefficiency, such that fewer particles of a sample are wasted (i.e.,disposing of particles, such as cells, such that they go unsorted) whensorting a sample. In some cases, the efficiency of sorting may beimproved such that more variations of cell phenotypes may be collectedand sorted when the subject systems and methods are employed. When usedas part of flow cytometrically sorting a sample, the subject methods canimprove the yield of particle sorting.

In practicing the subject methods, a sample having particles isirradiated with a light source and light from the sample is detectedwith a light detection system having one or more photodetectors. In someembodiments, the sample is a biological sample. The term “biologicalsample” is used in its conventional sense to refer to a whole organism,plant, fungi or a subset of animal tissues, cells or component partswhich may in certain instances be found in blood, mucus, lymphaticfluid, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolarlavage, amniotic fluid, amniotic cord blood, urine, vaginal fluid andsemen. As such, a “biological sample” refers to both the native organismor a subset of its tissues as well as to a homogenate, lysate or extractprepared from the organism or a subset of its tissues, including but notlimited to, for example, plasma, serum, spinal fluid, lymph fluid,sections of the skin, respiratory, gastrointestinal, cardiovascular, andgenitourinary tracts, tears, saliva, milk, blood cells, tumors, organs.Biological samples may be any type of organismic tissue, including bothhealthy and diseased tissue (e.g., cancerous, malignant, necrotic,etc.). In certain embodiments, the biological sample is a liquid sample,such as blood or derivative thereof, e.g., plasma, tears, urine, semen,etc., where in some instances the sample is a blood sample, includingwhole blood, such as blood obtained from venipuncture or fingerstick(where the blood may or may not be combined with any reagents prior toassay, such as preservatives, anticoagulants, etc.).

In certain embodiments the source of the sample is a “mammal” or“mammalian”, where these terms are used broadly to describe organismswhich are within the class Mammalia, including the orders carnivore(e.g., dogs and cats), Rodentia (e.g., mice, guinea pigs, and rats), andprimates (e.g., humans, chimpanzees, and monkeys). In some instances,the subjects are humans. The methods may be applied to samples obtainedfrom human subjects of both genders and at any stage of development(i.e., neonates, infant, juvenile, adolescent, adult), where in certainembodiments the human subject is a juvenile, adolescent or adult. Whilethe present invention may be applied to samples from a human subject, itis to be understood that the methods may also be carried-out on samplesfrom other animal subjects (that is, in “non-human subjects”) such as,but not limited to, birds, mice, rats, dogs, cats, livestock and horses.

In embodiments, a sample (e.g., in a flow stream of a flow cytometer) isirradiated with light from a light source. In some embodiments, thelight source is a broadband light source, emitting light having a broadrange of wavelengths, such as for example, spanning 50 nm or more, suchas 100 nm or more, such as 150 nm or more, such as 200 nm or more, suchas 250 nm or more, such as 300 nm or more, such as 350 nm or more, suchas 400 nm or more and including spanning 500 nm or more. For example,one suitable broadband light source emits light having wavelengths from200 nm to 1500 nm. Another example of a suitable broadband light sourceincludes a light source that emits light having wavelengths from 400 nmto 1000 nm. Where methods include irradiating with a broadband lightsource, broadband light source protocols of interest may include, butare not limited to, a halogen lamp, deuterium arc lamp, xenon arc lamp,stabilized fiber-coupled broadband light source, a broadband LED withcontinuous spectrum, superluminescent emitting diode, semiconductorlight emitting diode, wide spectrum LED white light source, a multi-LEDintegrated white light source, among other broadband light sources orany combination thereof.

In other embodiments, methods includes irradiating with a narrow bandlight source emitting a particular wavelength or a narrow range ofwavelengths, such as for example with a light source which emits lightin a narrow range of wavelengths like a range of 50 nm or less, such as40 nm or less, such as 30 nm or less, such as 25 nm or less, such as 20nm or less, such as 15 nm or less, such as 10 nm or less, such as 5 nmor less, such as 2 nm or less and including light sources which emit aspecific wavelength of light (i.e., monochromatic light). Where methodsinclude irradiating with a narrow band light source, narrow band lightsource protocols of interest may include, but are not limited to, anarrow wavelength LED, laser diode or a broadband light source coupledto one or more optical bandpass filters, diffraction gratings,monochromators or any combination thereof.

In certain embodiments, methods include irradiating the sample with oneor more lasers. As discussed above, the type and number of lasers willvary depending on the sample as well as desired light collected and maybe a gas laser, such as a helium-neon laser, argon laser, krypton laser,xenon laser, nitrogen laser, CO₂ laser, CO laser, argon-fluorine (ArF)excimer laser, krypton-fluorine (KrF) excimer laser, xenon chlorine(XeCl) excimer laser or xenon-fluorine (XeF) excimer laser or acombination thereof. In other instances, the methods include irradiatingthe flow stream with a dye laser, such as a stilbene, coumarin orrhodamine laser. In yet other instances, methods include irradiating theflow stream with a metal-vapor laser, such as a helium-cadmium (HeCd)laser, helium-mercury (HeHg) laser, helium-selenium (HeSe) laser,helium-silver (HeAg) laser, strontium laser, neon-copper (NeCu) laser,copper laser or gold laser and combinations thereof. In still otherinstances, methods include irradiating the flow stream with asolid-state laser, such as a ruby laser, an Nd:YAG laser, NdCrYAG laser,Er:YAG laser, Nd:YLF laser, Nd:YVO₄ laser, Nd:YCa₄O(BO₃)₃ laser, Nd:YCOBlaser, titanium sapphire laser, thulim YAG laser, ytterbium YAG laser,ytterbium₂O₃ laser or cerium doped lasers and combinations thereof.

The sample may be irradiated with one or more of the above-mentionedlight sources, such as 2 or more light sources, such as 3 or more lightsources, such as 4 or more light sources, such as 5 or more lightsources and including 10 or more light sources. The light source mayinclude any combination of types of light sources. For example, in someembodiments, the methods include irradiating the sample in the flowstream with an array of lasers, such as an array having one or more gaslasers, one or more dye lasers and one or more solid-state lasers.

The sample may be irradiated with wavelengths ranging from 200 nm to1500 nm, such as from 250 nm to 1250 nm, such as from 300 nm to 1000 nm,such as from 350 nm to 900 nm and including from 400 nm to 800 nm. Forexample, where the light source is a broadband light source, the samplemay be irradiated with wavelengths from 200 nm to 900 nm. In otherinstances, where the light source includes a plurality of narrow bandlight sources, the sample may be irradiated with specific wavelengths inthe range from 200 nm to 900 nm. For example, the light source may be aplurality of narrow band LEDs (1 nm-25 nm) each independently emittinglight having a range of wavelengths between 200 nm to 900 nm. In otherembodiments, the narrow band light source includes one or more lasers(such as a laser array) and the sample is irradiated with specificwavelengths ranging from 200 nm to 700 nm, such as with a laser arrayhaving gas lasers, excimer lasers, dye lasers, metal vapor lasers andsolid-state laser as described above.

Where more than one light source is employed, the sample may beirradiated with the light sources simultaneously or sequentially, or acombination thereof. For example, the sample may be simultaneouslyirradiated with each of the light sources. In other embodiments, theflow stream is sequentially irradiated with each of the light sources.Where more than one light source is employed to irradiate the samplesequentially, the time each light source irradiates the sample mayindependently be 0.001 microseconds or more, such as 0.01 microsecondsor more, such as 0.1 microseconds or more, such as 1 microsecond ormore, such as 5 microseconds or more, such as 10 microseconds or more,such as 30 microseconds or more and including 60 microseconds or more.For example, methods may include irradiating the sample with the lightsource (e.g., laser) for a duration which ranges from 0.001 microsecondsto 100 microseconds, such as from 0.01 microseconds to 75 microseconds,such as from 0.1 microseconds to 50 microseconds, such as from 1microsecond to 25 microseconds and including from 5 microseconds to 10microseconds. In embodiments where the sample is sequentially irradiatedwith two or more light sources, the duration the sample is irradiated byeach light source may be the same or different.

The time period between irradiation by each light source may also vary,as desired, being separated independently by a delay of 0.001microseconds or more, such as 0.01 microseconds or more, such as 0.1microseconds or more, such as 1 microsecond or more, such as 5microseconds or more, such as by 10 microseconds or more, such as by 15microseconds or more, such as by 30 microseconds or more and includingby 60 microseconds or more. For example, the time period betweenirradiation by each light source may range from 0.001 microseconds to 60microseconds, such as from 0.01 microseconds to 50 microseconds, such asfrom 0.1 microseconds to 35 microseconds, such as from 1 microsecond to25 microseconds and including from 5 microseconds to 10 microseconds. Incertain embodiments, the time period between irradiation by each lightsource is 10 microseconds. In embodiments where sample is sequentiallyirradiated by more than two (i.e., 3 or more) light sources, the delaybetween irradiation by each light source may be the same or different.

The sample may be irradiated continuously or in discrete intervals. Insome instances, methods include irradiating the sample in the samplewith the light source continuously. In other instances, the sample in isirradiated with the light source in discrete intervals, such asirradiating every 0.001 millisecond, every 0.01 millisecond, every 0.1millisecond, every 1 millisecond, every 10 milliseconds, every 100milliseconds and including every 1000 milliseconds, or some otherinterval.

Depending on the light source, the sample may be irradiated from adistance which varies such as 0.01 mm or more, such as 0.05 mm or more,such as 0.1 mm or more, such as 0.5 mm or more, such as 1 mm or more,such as 2.5 mm or more, such as 5 mm or more, such as 10 mm or more,such as 15 mm or more, such as 25 mm or more and including 50 mm ormore. Also, the angle or irradiation may also vary, ranging from 10° to90°, such as from 15° to 85°, such as from 20° to 80°, such as from 25°to 75° and including from 30° to 60°, for example at a 90° angle.

In certain embodiments, methods include irradiating the sample with twoor more beams of frequency shifted light. A light beam generatorcomponent may be employed having a laser and an acousto-optic device forfrequency shifting the laser light. In these embodiments, methodsinclude irradiating the acousto-optic device with the laser. Dependingon the desired wavelengths of light produced in the output laser beam(e.g., for use in irradiating a sample in a flow stream), the laser mayhave a specific wavelength that varies from 200 nm to 1500 nm, such asfrom 250 nm to 1250 nm, such as from 300 nm to 1000 nm, such as from 350nm to 900 nm and including from 400 nm to 800 nm. The acousto-opticdevice may be irradiated with one or more lasers, such as 2 or morelasers, such as 3 or more lasers, such as 4 or more lasers, such as 5 ormore lasers and including 10 or more lasers. The lasers may include anycombination of types of lasers. For example, in some embodiments, themethods include irradiating the acousto-optic device with an array oflasers, such as an array having one or more gas lasers, one or more dyelasers and one or more solid-state lasers.

Where more than one laser is employed, the acousto-optic device may beirradiated with the lasers simultaneously or sequentially, or acombination thereof. For example, the acousto-optic device may besimultaneously irradiated with each of the lasers. In other embodiments,the acousto-optic device is sequentially irradiated with each of thelasers. Where more than one laser is employed to irradiate theacousto-optic device sequentially, the time each laser irradiates theacousto-optic device may independently be 0.001 microseconds or more,such as 0.01 microseconds or more, such as 0.1 microseconds or more,such as 1 microsecond or more, such as 5 microseconds or more, such as10 microseconds or more, such as 30 microseconds or more and including60 microseconds or more. For example, methods may include irradiatingthe acousto-optic device with the laser for a duration which ranges from0.001 microseconds to 100 microseconds, such as from 0.01 microsecondsto 75 microseconds, such as from 0.1 microseconds to 50 microseconds,such as from 1 microsecond to 25 microseconds and including from 5microseconds to 10 microseconds. In embodiments where the acousto-opticdevice is sequentially irradiated with two or more lasers, the durationthe acousto-optic device is irradiated by each laser may be the same ordifferent.

The time period between irradiation by each laser may also vary, asdesired, being separated independently by a delay of 0.001 microsecondsor more, such as 0.01 microseconds or more, such as 0.1 microseconds ormore, such as 1 microsecond or more, such as 5 microseconds or more,such as by 10 microseconds or more, such as by 15 microseconds or more,such as by 30 microseconds or more and including by 60 microseconds ormore. For example, the time period between irradiation by each lightsource may range from 0.001 microseconds to 60 microseconds, such asfrom 0.01 microseconds to 50 microseconds, such as from 0.1 microsecondsto 35 microseconds, such as from 1 microsecond to 25 microseconds andincluding from 5 microseconds to 10 microseconds. In certainembodiments, the time period between irradiation by each laser is 10microseconds. In embodiments where the acousto-optic device issequentially irradiated by more than two (i.e., 3 or more) lasers, thedelay between irradiation by each laser may be the same or different.

The acousto-optic device may be irradiated continuously or in discreteintervals. In some instances, methods include irradiating theacousto-optic device with the laser continuously. In other instances,the acousto-optic device is irradiated with the laser in discreteintervals, such as irradiating every 0.001 millisecond, every 0.01millisecond, every 0.1 millisecond, every 1 millisecond, every 10milliseconds, every 100 milliseconds and including every 1000milliseconds, or some other interval.

Depending on the laser, the acousto-optic device may be irradiated froma distance which varies such as 0.01 mm or more, such as 0.05 mm ormore, such as 0.1 mm or more, such as 0.5 mm or more, such as 1 mm ormore, such as 2.5 mm or more, such as 5 mm or more, such as 10 mm ormore, such as 15 mm or more, such as 25 mm or more and including 50 mmor more. Also, the angle or irradiation may also vary, ranging from 10°to 90°, such as from 15° to 85°, such as from 20° to 80°, such as from25° to 75° and including from 30° to 60°, for example at a 90° angle.

In embodiments, methods include applying radiofrequency drive signals tothe acousto-optic device to generate angularly deflected laser beams.Two or more radiofrequency drive signals may be applied to theacousto-optic device to generate an output laser beam with the desirednumber of angularly deflected laser beams, such as 3 or moreradiofrequency drive signals, such as 4 or more radiofrequency drivesignals, such as 5 or more radiofrequency drive signals, such as 6 ormore radiofrequency drive signals, such as 7 or more radiofrequencydrive signals, such as 8 or more radiofrequency drive signals, such as 9or more radiofrequency drive signals, such as 10 or more radiofrequencydrive signals, such as 15 or more radiofrequency drive signals, such as25 or more radiofrequency drive signals, such as 50 or moreradiofrequency drive signals and including 100 or more radiofrequencydrive signals.

The angularly deflected laser beams produced by the radiofrequency drivesignals each have an intensity based on the amplitude of the appliedradiofrequency drive signal. In some embodiments, methods includeapplying radiofrequency drive signals having amplitudes sufficient toproduce angularly deflected laser beams with a desired intensity. Insome instances, each applied radiofrequency drive signal independentlyhas an amplitude from about 0.001 V to about 500 V, such as from about0.005 V to about 400 V, such as from about 0.01 V to about 300 V, suchas from about 0.05 V to about 200 V, such as from about 0.1 V to about100 V, such as from about 0.5 V to about 75 V, such as from about 1 V to50 V, such as from about 2 V to 40 V, such as from 3 V to about 30 V andincluding from about 5 V to about 25 V. Each applied radiofrequencydrive signal has, in some embodiments, a frequency of from about 0.001MHz to about 500 MHz, such as from about 0.005 MHz to about 400 MHz,such as from about 0.01 MHz to about 300 MHz, such as from about 0.05MHz to about 200 MHz, such as from about 0.1 MHz to about 100 MHz, suchas from about 0.5 MHz to about 90 MHz, such as from about 1 MHz to about75 MHz, such as from about 2 MHz to about 70 MHz, such as from about 3MHz to about 65 MHz, such as from about 4 MHz to about 60 MHz andincluding from about 5 MHz to about 50 MHz.

In these embodiments, the angularly deflected laser beams in the outputlaser beam are spatially separated. Depending on the appliedradiofrequency drive signals and desired irradiation profile of theoutput laser beam, the angularly deflected laser beams may be separatedby 0.001 μm or more, such as by 0.005 μm or more, such as by 0.01 μm ormore, such as by 0.05 μm or more, such as by 0.1 μm or more, such as by0.5 μm or more, such as by 1 μm or more, such as by 5 μm or more, suchas by 10 μm or more, such as by 100 μm or more, such as by 500 μm ormore, such as by 1000 μm or more and including by 5000 μm or more. Insome embodiments, the angularly deflected laser beams overlap, such aswith an adjacent angularly deflected laser beam along a horizontal axisof the output laser beam. The overlap between adjacent angularlydeflected laser beams (such as overlap of beam spots) may be an overlapof 0.001 μm or more, such as an overlap of 0.005 μm or more, such as anoverlap of 0.01 μm or more, such as an overlap of 0.05 μm or more, suchas an overlap of 0.1 μm or more, such as an overlap of 0.5 μm or more,such as an overlap of 1 μm or more, such as an overlap of 5 μm or more,such as an overlap of 10 μm or more and including an overlap of 100 μmor more.

In certain instances, the flow stream is irradiated with a plurality ofbeams of frequency-shifted light and a cell in the flow stream is imagedby fluorescence imaging using radiofrequency tagged emission (FIRE) togenerate a frequency-encoded image, such as those described in Diebold,et al. Nature Photonics Vol. 7(10); 806-810 (2013) as well as describedin U.S. Pat. Nos. 9,423,353; 9,784,661 and 10,006,852 and U.S. PatentPublication Nos. 2017/0133857 and 2017/0350803, the disclosures of whichare herein incorporated by reference.

As discussed above, in embodiments light from the irradiated sample isconveyed to a light detection system as described in greater detailbelow and measured by the plurality of photodetectors. In someembodiments, methods include measuring the collected light over a rangeof wavelengths (e.g., 200 nm-1000 nm). For example, methods may includecollecting spectra of light over one or more of the wavelength ranges of200 nm-1000 nm. In yet other embodiments, methods include measuringcollected light at one or more specific wavelengths. For example, thecollected light may be measured at one or more of 450 nm, 518 nm, 519nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm andany combinations thereof. In certain embodiments, methods includingmeasuring wavelengths of light which correspond to the fluorescence peakwavelength of fluorophores. In some embodiments, methods includemeasuring collected light across the entire fluorescence spectrum ofeach fluorophore in the sample.

The collected light may be measured continuously or in discreteintervals. In some instances, methods include taking measurements of thelight continuously. In other instances, the light is measured indiscrete intervals, such as measuring light every 0.001 millisecond,every 0.01 millisecond, every 0.1 millisecond, every 1 millisecond,every 10 milliseconds, every 100 milliseconds and including every 1000milliseconds, or some other interval.

Measurements of the collected light may be taken one or more timesduring the subject methods, such as 2 or more times, such as 3 or moretimes, such as 5 or more times and including 10 or more times. Incertain embodiments, the light propagation is measured 2 or more times,with the data in certain instances being averaged.

Light from the sample may be measured at one or more wavelengths of,such as at 5 or more different wavelengths, such as at 10 or moredifferent wavelengths, such as at 25 or more different wavelengths, suchas at 50 or more different wavelengths, such as at 100 or more differentwavelengths, such as at 200 or more different wavelengths, such as at300 or more different wavelengths and including measuring the collectedlight at 400 or more different wavelengths.

Methods of the present disclosure include flow cytometrically sorting asample with particles, such as cells, based on order of identification.In embodiments, phenotypes of cells in the sample flowing in the flowstream are identified based on one or more data signals generated fromdetected light from the cells. The data signals generated may be analogdata signals or digital data signals. Where the data signals are analogdata signals, in some instances, methods include converting the analogdata signals to digital data signals, such as with an analog-to-digitalconverter. In some instances, identifying a cell phenotype includesassigning the cell to a cell population cluster. In other instances,identifying a cell phenotype includes plotting one or more data signalsgenerated from detected light of the cell onto a scatter plot. Incertain instances, identifying cell phenotypes in the sample includesgenerating a two-dimensional bitmap having a region of interest (ROI)and determining whether a particle should be assigned to the ROI of thebitmap.

In embodiments, cells of the sample that have a phenotype of acollection of predetermined phenotypes are dynamically sorted based onorder of identification. In some instances, the collection ofpredetermined phenotypes may include one or more distinct anddistinguishable cell phenotypes, such as one cell phenotype, such as twocell phenotypes, such as 16 cell phenotypes, such as 256 cell phenotypesor more. For example, the collection of cell phenotypes may be acollection of 32 different cell phenotypes. In embodiments, a firstphenotype may be excluded from the collection of phenotypes aftersorting a predetermined number of cells that have the first phenotype.In some instances, the predetermined number of cells that have the firstphenotype may be one or more. For example, the predetermined number ofcells may be one or two or ten or 100 or more.

Upon “excluding a first phenotype from the collection of phenotypes,”the collection of predetermined phenotypes no longer includes the firstphenotype such that, for example, if, prior to excluding the firstphenotype, the collection of phenotypes included 64 phenotypes, thenafter excluding the first phenotype, the collection of phenotypes wouldinclude 63 phenotypes. Further, after a first phenotype is excluded fromthe collection of phenotypes, upon continued sorting of the sample inthe flow stream, cells that have a first phenotype would no longer havea phenotype included in the collection of phenotypes since the firstphenotype was excluded from the collection of phenotypes, and, as aresult, cells having the first phenotype would no longer be sorted intopartitions.

By “sorting based on order of identification,” it is meant, for example,sorting cells in the order in which such cells appear in, for example, aflow stream. In other words, cells may be sorted as and when they appearin, for example, a flow stream during sorting. That is, in someembodiments, upon identifying the phenotype of a cell in the flowstream, the cell phenotype may be compared against each phenotypecomprising the collection of phenotypes and sorted into a partition uponconfirmation that the cell phenotype is included in the collection ofphenotypes. In some cases, a result of dynamically sorting based onorder of identification is that the order in which cell phenotypes areidentified and sorted is not determined in advance of commencing sortinga sample, but, instead, is determined “dynamically” or currently withsorting. In some cases, the order in which cells are identified andsorted based on order of identification may vary from one sample toanother.

In contrast, for example, when cells are not sorted dynamically based onorder of identification according to the present disclosure, they may besorted into a plurality of partitions where it is predetermined prior tocommencing sorting which cell phenotypes will be sorted into whichpartition. For example, when cells are not sorted dynamically based onorder of identification according to the present disclosure, a cell witha phenotype identified as belonging to a collection of cell phenotypesto be sorted but nonetheless not having the phenotype assigned to thenext partition, may be disposed of without being sorted. When cells aredynamically sorted based on order of identification in accordance withthe present disclosure, the next partition would be dynamically assignedsuch cell phenotype, and the identified cell would be sortedaccordingly. As a result, the identified cell would not be wasted.

FIG. 5 depicts two example sorting methods, one example 501 wheresorting occurs dynamically based on order of identification, as incertain embodiments of the methods of the present disclosure, andanother example 502 where sorting does not occur dynamically based onorder of identification.

Multi-well plate 510 into which cells are sorted according to a methodwhere sorting does not occur dynamically based on order ofidentification includes eight wells, identified by their rows, “A” or“B” and their columns “1”, “2”, “3” or “4”. Each well is assigned astring consisting of three characters, a combination of “+”s and “−”s.Each position within the string indicates a constituent characteristicof a cell phenotype, where the “+” or “−” indicates the presence orabsence, respectively, of such characteristic of a cell phenotype. Eachwell of multi-well plate 510 is assigned a cell phenotype correspondingto the well in advance of commencing sorting. That is, when practicingsuch a method, both (i) determining a collection of cell phenotypes aswell as (ii) an assignment of each phenotype to each well occurs inadvance of commencing cell sorting. Such assignment in advance ofsorting is emphasized in FIG. 5 for illustrative purposes by the order,corresponding to counting in binary from left to right within each row,of cell phenotypes among the wells. Upon commencing cell sorting at, forexample, well A1, cells in the flow stream are identified and discardeduntil the first instance occurs in the flow stream of a cell identifiedas exhibiting a phenotype corresponding to “−−−”, the cell phenotypeassigned to well A1. After sorting a predetermined number of cellsexhibiting a phenotype corresponding to “−−−”, cells in the flow streamare identified and discarded until the first instance occurs in the flowstream of a cell identified as exhibiting a phenotype corresponding to“−−+”, the cell phenotype assigned to well A2. The sorting processcontinues as such.

Multi-well plate 520 into which cells are sorted according to a methodwhere sorting does occur dynamically based on order of identificationaccording to embodiments of the present disclosure also includes eightwells, identified by their rows, “A” or “B” and their columns “1”, “2”,“3” or “4”. The cell phenotypes labeled as corresponding to each wellare assigned to each well during cell sorting, i.e., cells aredynamically sorted. That is, instead of such phenotypes being assignedin advance, each well of multi-well plate 520 is assigned a cellphenotype dynamically while sorting is ongoing based on the order inwhich a cell belonging to a collection of predetermined phenotypes isidentified in the flow stream. When practicing such a method, onlydetermining a collection of cell phenotypes must occur in advance ofcommencing cell sorting, and not the assignment of a cell phenotype toeach well in advance. In the instant example, if sorting began at wellA1, then the first cell in the flow stream identified as belonging tothe collection of predetermined phenotypes had a phenotype of “−++”. Theapparent random order of cell phenotypes distributed across wells ofmulti-well plate 520 illustrates the unpredictable order in which cellsare identified in the flow stream during sorting.

In some embodiments, a predetermined number of cells are sorted into apartition. For instance, the predetermined number may be one or more.For example, a single cell may be sorted into a partition or two cellsor ten cells or 100 cells or more may be sorted into a partition. Insome cases, partitions comprise wells. Wells may be any size with thecapacity of holding particles, such as cells. For example, well volumemay be 0.001 mL or greater, such as 0.005 mL or 0.015 mL or 0.1 mL or 2mL or 5 mL or greater. In some embodiments, the wells may be wells of amulti-well plate. A multi-well plate may include any number of wells. Ininstances, a multi-well plate may include six or 12 or 24 or 48 or 96 or384 or 1536 or 3456 or 9600 or more wells. Wells of a multi-well platemay be arranged in any convenient pattern. In some instances, wells arearranged in a rectangular shape with a length to width ration ofapproximately two to three. In some instances, multi-well plates of thepresent disclosure may conform to accepted standards such as thestandard established by the Society for Biomolecular Sciences with theANSI-Standards. Multi-well plates may be composed of any convenientmaterial. In some cases, multi-well plates may be composed ofpolypropylene, polystyrene or polycarbonate. In some embodiments, themulti-well plate is advanced to a second well after sorting apredetermined number of cells into a first well.

Certain embodiments also include recording the phenotypes of cellssorted into partitions. In some cases, representations of cellphenotypes are recorded by storing a representation of a cell phenotypeupon sorting a cell having such phenotype. For example, some embodimentsmay record the phenotypes of cells sorted into partitions by addingrepresentations of phenotypes of sorted cells to a list of phenotypes.By representations of phenotypes, it is meant any practical summary of acell phenotype capable of distinguishing such cell phenotype from othercell phenotypes capable of being identified. For example, arepresentation of a cell phenotype may include a bit string where eachposition in the bit string corresponds to an individual constituentcharacteristic of a cell phenotype. The bits of such a bit string wouldindicate whether the constituent characteristics of a cell phenotype arepresent or absent. For example, a bit position may be set to “1” whenthe corresponding characteristic of the cell phenotype is present and“0” when absent.

The list of sorted cell phenotypes may take any convenient form. Forexample, a list may comprise an array of bit strings, where each bitstring corresponds to a representation of a phenotype of a sorted cell.The array may be any convenient size and may vary. For example, in someinstances, the size of the array may correspond to the number ofphenotypes in the collection of phenotypes multiplied by thepredetermined number of cells to be sorted having each phenotype. Insome cases, the size of the array may be increased as sortingprogresses. In other instances, a list may comprise a linked list, whereeach element of the linked list includes a representation of a phenotypeof a sorted cell as well as a link to the next entry in the linked list,if any.

In some embodiments, this method may further include querying the listof phenotypes to determine whether or not a first cell has already beensorted, wherein the first cell has a phenotype also exhibited by asecond cell. By querying the list of phenotypes, it is meant that thelist is searched to determine whether the list contains an identifiedphenotype. In some cases, an implication of the list including suchphenotype is that a first cell with such phenotype has already beensorted. The list of sorted phenotypes can be queried in any convenientmanner. For example, the list can be traversed in order from first tolast entry, comparing each sorted cell phenotype with a newly identifiedcell phenotype.

Some embodiments of the method of the present disclosure may record thephenotypes of cells sorted into partitions by adding representations ofphenotypes of sorted cells to a probabilistic data structure. Byprobabilistic data structure, it is meant, for example, a data structurethat, in some cases, is only capable of returning probabilistic and notdefinitive information about the properties, such as the contents, ofthe data structure. For example, in response to querying whether anelement is a member of a set represented by a probabilistic datastructure, the probabilistic data structure may be capable of indicatingonly that the element is possibly contained in the set or definitely notin the set. In some embodiments, the probabilistic data structure may bea Bloom filter. The Bloom filter may be any convenient size, and, insome cases, the size of the Bloom filter may be determined based, inpart, on size and accuracy constraints. In some embodiments, the hashfunctions for the Bloom filter may be selected to reduce collisions inthe Bloom filter based on an analysis of the sample. By collisions inthe Bloom filter, it is meant that different cell phenotypes map to thesame entry or entries in the Bloom filter. Such conditions maypotentially lead to inaccurate results, such as false positives, fromthe Bloom filter. By selecting hash functions based on an analysis ofthe sample, it is meant selecting hash functions in order to reduce orminimize the possibility of different cell phenotypes being mapped intothe same entry or entries of the Bloom filter.

In some embodiments, this method may further include querying theprobabilistic data structure to determine whether or not a first cellhas already been sorted, wherein the first cell has a phenotype alsoexhibited by a second cell. By querying the probabilistic datastructure, it is meant that the data structure is accessed to determinewhether it contains an identified phenotype. For instance, in the caseof a Bloom filter, a representation of the identified phenotype is used,for example by hashing it, to access the contents of the Bloom filter todetermine whether such phenotype has already been sorted. In someinstances, the probabilistic data structure may only be capable ofindicating whether the phenotype has likely already been sorted. In somecases, an implication of the probabilistic data structure indicating itincludes such phenotype is that a first cell with such phenotype hasalready been sorted.

Some embodiments of the method of the present disclosure may record thephenotypes of cells sorted into partitions by adding representations ofphenotypes of sorted cells to an associative array. By associativearray, it is meant a data structure comprising a collection of key-valuepairs. In some instances, the associative array may be comprised of, forexample, a hash table or a search tree, such as a binary search tree, oran array. In some embodiments, the associative array is a contentaddressable memory. By a content addressable memory, it is meant a pieceof hardware technology, such as a semiconductor implementation, capableof comparing an input search term against the contents of the contentaddressable memory and returning confirmation of whether the search termis stored in the content addressable memory.

In some embodiments, this method may further include querying theassociative array to determine whether or not a first cell has alreadybeen sorted, wherein the first cell has a phenotype also exhibited by asecond cell. By querying the associative array, it is meant accessingthe associative array by looking up an identified phenotype in theassociative array to determine whether it contains the identifiedphenotype. For instance, when the associative array is comprised of asearch tree, such search tree may be searched to determine whether itincludes the identified phenotype. In some cases, an implication of theassociative array indicating it includes such phenotype is that a firstcell with such phenotype has already been sorted.

Some embodiments of the method of the present disclosure may record thephenotypes of cells sorted into partitions by setting a value at alocation in a truth table corresponding to a cell phenotype. By truthtable, it is meant an array-like structure or table that is accessed byrepresentations of cell phenotypes and with Boolean values correspondingto each row of the table. For example, when a cell phenotype isrepresented as a binary bit string, such bit string may be used toaccess a row in the truth table. Upon accessing a row, the truth table,which contains a Boolean value, such as true or false, corresponding tosuch row of the trust table, returns the Boolean value associated withsuch row. In some instances, a “true” value at a row of the truth tablemay be defined to mean that the cell phenotype used to access that rowhas already been sorted, and a “false” value means it has not alreadybeen sorted. In some instances, upon sorting a cell with a particularphenotype, the corresponding entry in the truth table may then be set to“true” to indicate that a cell with such phenotype has been sorted.

In some embodiments, this method may further include querying the truthtable to determine whether or not a first cell has already been sorted,wherein the first cell has a phenotype also exhibited by a second cell.By querying the truth table, it is meant using a representation of thecell phenotype, such as, for example, a binary string, to access acorresponding row in the truth table to determine whether the valueassociated with that row of the truth table indicates the identifiedphenotype has already been sorted. In some cases, an implication of thetruth table indicating the phenotype has been sorted is that a firstcell with such phenotype has already been sorted.

In some embodiments, the methods of the present disclosure furthercomprise recording information identifying a partition and a phenotypeof a cell sorted into the partition. For example, in some instances, themethod may comprise maintaining a list identifying partitions andphenotypes of cells sorted into the partitions. In instances, a cellphenotype may be summarized in a representation that is distinct anddistinguishable from representations of other possible cell phenotypescapable of being identified and, similarly, the partition into which acell phenotype is sorted may be summarized in a representation that isdistinct and distinguishable from other partitions into which cells aresorted. For example, cell phenotypes may be represented, in some cases,in the form of a bit string where each bit position of the bit stringrepresents the presence or absence of a measurable feature (i.e., aconstituent characteristic) of the cell phenotype such that thecollection of measurable features (i.e., constituent characteristics) ofthe cell phenotype, together, is capable of representing distinguishablecell phenotypes. Similarly, information identifying a partition may be asummary of the position of the partition. For example, in instances whenthe partition comprises a well of a multi-well plate, informationidentifying the partition may include, for example, the position of thewell on the multi-well plate, such as horizontal and verticalcoordinates, as well as, in some cases, information identifying onemulti-well plate among several multi-well plates. By maintaining a listof cell phenotypes and corresponding partitions, it is meant thatrepresentations of cell phenotypes and representations of correspondingpartition identifying information may be stored in, for example, a datastructure, such as an array, a linked list or a binary search tree. Ininstances where the list of cell phenotypes and corresponding partitionsis maintained in the form of an array, each array entry may comprise arepresentation of a cell phenotype and a partition. In instances wherethe list of cell phenotypes and corresponding partitions is maintainedin the form of a linked list, each linked list node may comprise arepresentation of a cell phenotype and a partition. In instances wherethe list of cell phenotypes and corresponding partitions is maintainedin the form of a binary search tree, each tree node may comprise arepresentation of a cell phenotype and a partition.

In some embodiments, the method of the present disclosure furthercomprises estimating, based on a subset of cells in the sample, a numberof phenotypes exhibited by cells in the sample. By a subset of cells inthe sample, it is meant any convenient amount of the sample containingcells that may be needed for produce such an estimate. In some cases, assmall an amount of the sample as possible is used to estimate the numberof phenotypes of cells in the sample. By a number of phenotypesexhibited by cells in the sample, it is meant the number of differentand distinct variations of cell phenotypes capable of identificationthat are present in the sample. In some instances, it may be beneficialto estimate in advance the number of different cell phenotypes exhibitedby cells comprising a sample in order to more efficiently sort thesample or to insure a sufficient number of partitions are available tostore sorted cells or to more efficiently record the cell phenotypessorted.

The number of phenotypes exhibited by cells in the sample may beestimated using any convenient cardinality estimation algorithm,including, for example, probabilistic counting algorithms. In someembodiments, the Flajolet-Martin algorithm may be used to estimate thenumber of distinct phenotypes in a sample based on a subset of cells inthe sample. In other embodiments, refinements of the Flajolet-Martinalgorithm may be used, such as, for example, the HyperLogLog or theHyperLogLog++ algorithms. In some instances, the estimate of the numberof distinct phenotypes in a sample may be computed based on aggregatinga plurality of results computed from the application of one or morecardinality estimation algorithms. In such cases, the estimate of thenumber of distinct phenotypes in a sample may be, for example, anaverage, of the results of the plurality of estimates computed by theone or more cardinality estimation algorithms.

In some embodiments of the present invention, sorting cells comprisesdeflecting cells into a first partition and a second partition. Bydeflecting cells, it is meant directing sorted cells by means of, forexample, an electrostatic deflector of a cell sorter, as describedherein, into one of either a first partition or a second partition. Inthese methods, in some cases, the cells in the sample may be dividedinto a first group that exhibits a first collection of phenotypes,comprising a predetermined number of phenotypes, and a second group thatdoes not exhibit the first collection of phenotypes. Further, thesemethods may also comprise that the first group is sorted into one of thefirst partition or the second partition, based on order ofidentification. In such instances, sorting a sample according to suchmethod results in either the first or the second partition comprisingcells exhibiting phenotypes belonging to the first collection ofphenotypes and, in contrast, the other partition comprising cellsexhibiting phenotypes that do not belong to the first collection ofphenotypes. In instances, the first collection of phenotypes maycomprise one or more identifiable cell phenotypes, such as a single cellphenotype or two cell phenotypes or ten cell phenotypes or 100 cellphenotypes or more. By sorting based on order of identification, it ismeant that a cell sorted into the first partition may be either a memberof the first group or the second group of cell phenotypes, depending onthe order in which cells belonging to either the first group or thesecond group appear in the flow stream. In certain instances, the firstcollection of phenotypes used to divide cells into a first group and asecond group is determined so that the first group and the second groupcomprise substantially the same number of cells. By comprisingsubstantially the same number of cells, it is meant that upon completionof sorting, both the first partition and the second partition containsubstantially the same number of cells, such that cells to be sorted inthe sample are divided substantially equally between the first andsecond partitions. In some instances, this method further comprisesdynamically updating the first collection of phenotypes based on sortedcells in the sample so that the first group and the second groupcomprise substantially the same number of cells. For instance, if, aftercommencing sorting, it is determined that the first partition in whichcells belonging to the first collection of phenotypes are sorted isaccruing substantially more or less cells than the second partition,then the phenotypes comprising the first collection of phenotypes may beadjusted to address the different number of cells in each of the firstand second partitions.

In some instances, plate sorting a single well at a time is not asefficient as sorting two wells at a time by having two distinctdeflections and then sorting pairs of wells, for example, sorting wellslabeled A1 and A2, followed by wells A3 and A4. In such embodiments, itmay be advantageous for sorting to both well deflections to beprobabilistically balanced so that both deflections are likely toachieve a sort within a similar time window. In such embodiments, thephenotypes to be sorted into wells may be partitioned into twopartitions where both partitions have a high probability of one of theconstituent phenotypes being sorted. For example, a hypothetical samplemay comprise sorting with Red—30%, Blue—25%, Green—20%, Yellow—15% andViolet—10%. In such hypothetical sample, Red plus Green would comprise50%, Blue plus Yellow plus Violet would comprise 50%. In this example,since Red and Blue are the most likely occurrences, they may be assignedto different partitions. If Red and Blue are sorted into the first twowells then the likely next two partitions would be Green (20%), andYellow plus Violet (25%). Other means of partitioning a sample may beapplied. For example, in some instances, refinements of partitioningbased on limited decision-making resources may be applied (e.g., thepartition could split based on positive or negative of some property ofthe cell) that enables the best use of finite resources, such as finiteresources in an implementation.

In certain embodiments, assigning partitions might be done with a methodsimilar to that employed with Huffman coding. In other embodiments,assigning partitions might be done using other approaches, such asarithmetic coding. In other embodiments, still other approaches may beapplied, for example approaches that take into consideration underlyingresource limitations, such as, for example, hardware or softwarelimitations.

In some embodiments, for each pair of wells to be sorted, the firstphenotype identified is sorted into the first well, after which thatphenotype is exclusively selected for the first well, and that phenotypeis excluded from sorting into the second and subsequent wells. In otherembodiments, the target phenotypes may be partitioned by balancing thepartitions based on frequency-of-occurrence.

Still other embodiments may sort partitions into more than two wells.

In cases in which one partition was sorted but the other partition wastaking an appreciable time and had not yet been sorted into, someembodiments may pause the sort temporarily after a predetermined timeoutperiod, move the plate so that a new well is accessible to the streamdeflection, and re-update the frequency partitioning. The predeterminedtimeout period may be set to any convenient amount of time. In someinstances, the predetermined timeout period may be based on the observedfrequency of phenotypes in a sample and an expected time to sort suchphenotypes. Embodiments may, for example, proceed such that well A1 issorted first, after which well A2 would be sorted; if A1 were taking anappreciable time and had not yet been sorted into, the sort may bepaused and the plate moved so that wells A2 and A3 are the target wells,and the phenotype sorted into well A1 would now be excluded fromsorting. Still other embodiments with a plurality of deflections may beapplied to a scenario where wells A1 and A2 are current wells; A2 sortsfirst, but the sorting process cannot move because well A1 is still tosort, and well A3 may then be enabled to sort.

In embodiments, methods include identifying cell phenotypes based on oneor more values of parameters that are determined from the data signalsgenerated from the detected light. In some embodiments determining oneor more parameters of the cell includes resolving light from a pluralityof fluorophores in the sample, such as resolving light detected fromfluorophores having overlapping fluorescence. In some embodiments,determining parameters of cells in the flow stream includes calculatinga spectral unmixing matrix for the fluorescence from the sample. In someinstances, certain values of a parameter of a cell indicate anindeterminate state of whether the cell has a phenotype. Byindeterminate state of whether a cell has a phenotype, it is meant thatit can neither be ruled in nor can it be ruled out whether a cellexhibits a particular phenotype or characteristic. In some instances,the boundaries of parameter values corresponding to distinguishingwhether a cell exhibits a particular phenotype from a cell that does notexhibit such phenotype are ambiguous. In such instances, certainparameter values may correspond to an indeterminate state of whether thesorted cell exhibits a particular phenotype. These methods may furthercomprise not sorting a cell when it exhibits an indeterminate state ofwhether the cell has a phenotype. By not sorting such a cell, it ismeant that the cell is not directed to a partition and instead may bediscarded, such as, for example, directed to the waste container of thecell sorter.

FIG. 6 depicts a plot 601 of at least two parameter values based on datasignals generated from light detected from cells of the sample, wherein,for example, the value of one parameter may be plotted on the horizontalaxis and the value of another parameter may be plotted on the verticalaxis. In this case a determination of whether a cell exhibits aphenotype and in particular, a marker corresponding to an aspect of acell phenotype, may be determined based on the position of a point onthe plot where the point corresponds to parameter values of the cell.The area on plot 601 proximate to area 610 corresponds to an affirmativedetermination that a marker is expressed in a cell and that the cell hasa phenotype comprising the marker of interest, i.e., that a constituentcharacteristic of a cell phenotype is present. The area on plot 601proximate to area 620 corresponds to an affirmative determination thatthe same marker is not expressed in a cell and that the cell has aphenotype that does not comprise the marker of interest, i.e., that theconstituent characteristic of a cell phenotype is not present. The areaon plot 601 proximate to area 630 corresponds to an indeterminatedetermination of whether the marker is expressed or not in a cell, i.e.,it is indeterminate whether or not the constituent characteristic of acell phenotype is present or not.

Some embodiments may further comprise estimating an amount offluorescent spillover measured with respect to a single data signalbased on a predicted variance-covariance matrix of unmixed data for asorted cell. In some instances, such method may further compriseestimating a covariance between two fluorophores based on the predictedvariance-covariance matrix of unmixed data for the sorted cell. Someembodiments of such method further comprise defining thresholds forexpression for a fluorophore based on the estimated covariance betweenfluorophores. Some embodiments of such method further comprise defininga measure of uncertainty with respect to the phenotype exhibited by asorted cell based on the estimated covariance between fluorophores.

In some embodiments of the method of the present disclosure, thecollection of predetermined phenotypes comprises a cell type with one ormore cell subtypes. By cell types, it is meant a classification of cellsused to distinguish between morphologically or phenotypically distinctcell forms within a species. In instances, the cell type may be T cell.In such methods, the cell subtypes may include, for example, CD4+ Tcells or CD8+ T cells.

FIG. 7 depicts a flow diagram for sorting a sample according to certainembodiments of the present disclosure. At step 701, a collection of cellphenotypes is selected. That is, one or more cell phenotypes aredesignated as belonging to a predetermined collection of phenotypes,meaning cells of the sample with phenotypes belonging to thepredetermined collection of phenotypes will be sorted if present in thesample. At step 702, sorting is commenced. When sorting is commenced, asample may be flowed in a flow stream of a flow cytometer. The samplemay be irradiated, and light may be detected from cells in the sampleflowing in the flow stream. Based on such light detected from cells inthe flow stream, a phenotype of the cell is identified at step 703. Atstep 704, it is determined whether the identified cell phenotype belongsto the predetermined collection of phenotypes selected in step 701. Inthe event the identified cell phenotype belongs to the predeterminedcollection of phenotypes, the process proceeds to step 705 and the cellis sorted into a partition. If, in the alternative, the identified cellphenotype does not belong to the predetermined collection of phenotypes,the process proceeds to step 706 and the cell is not sorted into apartition; it is discarded.

In some embodiments, methods for sorting components of sample includesorting particles (e.g., cells in a biological sample) with a particlesorting module having deflector plates, such as described in U.S. PatentPublication No. 2017/0299493, filed on Mar. 28, 2017, the disclosure ofwhich is incorporated herein by reference. In certain embodiments, cellsof the sample are sorted using a sort decision module having a pluralityof sort decision units, such as those described in U.S. ProvisionalPatent Application No. 62/803,264, filed on Feb. 8, 2019, the disclosureof which is incorporated herein by reference.

Systems for Flow Cytometrically Sorting a Sample with Particles, Such asCells, Based on Order of Identification

As summarized above, aspects of the present disclosure include a systemthat is configured to flow cytometrically sort a sample with particles,such as cells, based on order of identification. In particular, thepresent disclosure includes systems configured to dynamically sort intopartitions particles, such as cells, of the sample that have a phenotypeof a collection of predetermined phenotypes based on order ofidentification. As described above, the phrase “dynamically sortingbased on order of identification” is used to refer to sorting particles,such as cells, of interest in the order in which such cells appear in,for example, a flow stream. That is, particles, such as cells, ofinterest are sorted as and when they appear in, for example, a flowstream during sorting. Systems according to certain embodiments includea light source configured to irradiate a sample comprising cells flowingin a flow stream, a light detection system having a photodetector thatdetects light from the cells in the sample and generates a plurality ofdata signals from the detected light, a cell sorter configured toreceive the sample comprising cells flowing in the flow stream, aplurality of partitions configured to receive cells from the samplesorted by the cell sorter and a processor having memory operably coupledto the processor where the memory includes instructions stored thereonwhich when executed by the processor cause the processor to identifyphenotypes of cells in the sample based on one or more of the datasignals generated from the detected light and instruct the cell sorterto dynamically sort into the partitions cells of the sample that have aphenotype of a collection of predetermined phenotypes based on order ofidentification.

In embodiments, the light source may be any suitable broadband or narrowband source of light. Depending on the components in the sample (e.g.,cells, beads, non-cellular particles, etc.), the light source may beconfigured to emit wavelengths of light that vary, ranging from 200 nmto 1500 nm, such as from 250 nm to 1250 nm, such as from 300 nm to 1000nm, such as from 350 nm to 900 nm and including from 400 nm to 800 nm.For example, the light source may include a broadband light sourceemitting light having wavelengths from 200 nm to 900 nm. In otherinstances, the light source includes a narrow band light source emittinga wavelength ranging from 200 nm to 900 nm. For example, the lightsource may be a narrow band LED (1 nm-25 nm) emitting light having awavelength ranging between 200 nm to 900 nm. In certain embodiments, thelight source is a laser. In some instances, the subject systems includea gas laser, such as a helium-neon laser, argon laser, krypton laser,xenon laser, nitrogen laser, CO₂ laser, CO laser, argon-fluorine (ArF)excimer laser, krypton-fluorine (KrF) excimer laser, xenon chlorine(XeCl) excimer laser or xenon-fluorine (XeF) excimer laser or acombination thereof. In other instances, the subject systems include adye laser, such as a stilbene, coumarin or rhodamine laser. In yet otherinstances, lasers of interest include a metal-vapor laser, such as ahelium-cadmium (HeCd) laser, helium-mercury (HeHg) laser,helium-selenium (HeSe) laser, helium-silver (HeAg) laser, strontiumlaser, neon-copper (NeCu) laser, copper laser or gold laser andcombinations thereof. In still other instances, the subject systemsinclude a solid-state laser, such as a ruby laser, an Nd:YAG laser,NdCrYAG laser, Er:YAG laser, Nd:YLF laser, Nd:YVO₄ laser, Nd:YCa₄O(BO₃)₃laser, Nd:YCOB laser, titanium sapphire laser, thulim YAG laser,ytterbium YAG laser, ytterbium₂O₃ laser or cerium doped lasers andcombinations thereof.

In other embodiments, the light source is a non-laser light source, suchas a lamp, including but not limited to a halogen lamp, deuterium arclamp, xenon arc lamp, a light-emitting diode, such as a broadband LEDwith continuous spectrum, superluminescent emitting diode, semiconductorlight emitting diode, wide spectrum LED white light source, a multi-LEDintegrated light source. In some instances, the non-laser light sourceis a stabilized fiber-coupled broadband light source, white lightsource, among other light sources or any combination thereof.

The light source may be positioned any suitable distance from the sample(e.g., the flow stream in a flow cytometer), such as at a distance of0.001 mm or more from the flow stream, such as 0.005 mm or more, such as0.01 mm or more, such as 0.05 mm or more, such as 0.1 mm or more, suchas 0.5 mm or more, such as 1 mm or more, such as 5 mm or more, such as10 mm or more, such as 25 mm or more and including at a distance of 100mm or more. In addition, the light source may irradiate the sample atany suitable angle (e.g., relative the vertical axis of the flowstream), such as at an angle ranging from 10° to 90°, such as from 15°to 85°, such as from 20° to 80°, such as from 25° to 75° and includingfrom 30° to 60°, for example at a 90° angle. The light source may beconfigured to irradiate the sample continuously or in discreteintervals. In some instances, systems include a light source that isconfigured to irradiate the sample continuously, such as with acontinuous wave laser that continuously irradiates the flow stream atthe interrogation point in a flow cytometer. In other instances, systemsof interest include a light source that is configured to irradiate thesample at discrete intervals, such as every 0.001 milliseconds, every0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10milliseconds, every 100 milliseconds and including every 1000milliseconds, or some other interval. Where the light source isconfigured to irradiate the sample at discrete intervals, systems mayinclude one or more additional components to provide for intermittentirradiation of the sample with the light source. For example, thesubject systems in these embodiments may include one or more laser beamchoppers, manually or computer controlled beam stops for blocking andexposing the sample to the light source.

In some embodiments, the light source is a laser. Lasers of interest mayinclude pulsed lasers or continuous wave lasers. For example, the lasermay be a gas laser, such as a helium-neon laser, argon laser, kryptonlaser, xenon laser, nitrogen laser, CO₂ laser, CO laser, argon-fluorine(ArF) excimer laser, krypton-fluorine (KrF) excimer laser, xenonchlorine (XeCl) excimer laser or xenon-fluorine (XeF) excimer laser or acombination thereof; a dye laser, such as a stilbene, coumarin orrhodamine laser; a metal-vapor laser, such as a helium-cadmium (HeCd)laser, helium-mercury (HeHg) laser, helium-selenium (HeSe) laser,helium-silver (HeAg) laser, strontium laser, neon-copper (NeCu) laser,copper laser or gold laser and combinations thereof; a solid-statelaser, such as a ruby laser, an Nd:YAG laser, NdCrYAG laser, Er:YAGlaser, Nd:YLF laser, Nd:YVO₄ laser, Nd:YCa₄O(BO₃)₃ laser, Nd:YCOB laser,titanium sapphire laser, thulim YAG laser, ytterbium YAG laser,ytterbium₂O₃ laser or cerium doped lasers and combinations thereof; asemiconductor diode laser, optically pumped semiconductor laser (OPSL),or a frequency doubled or frequency tripled implementation of any of theabove mentioned lasers.

In certain embodiments, the light source is a light beam generator thatis configured to generate two or more beams of frequency shifted light.In some instances, the light beam generator includes a laser, aradiofrequency generator configured to apply radiofrequency drivesignals to an acousto-optic device to generate two or more angularlydeflected laser beams. In these embodiments, the laser may be a pulsedlasers or continuous wave laser, such as described above.

The acousto-optic device may be any convenient acousto-optic protocolconfigured to frequency shift laser light using applied acoustic waves.In certain embodiments, the acousto-optic device is an acousto-opticdeflector. The acousto-optic device in the subject system is configuredto generate angularly deflected laser beams from the light from thelaser and the applied radiofrequency drive signals. The radiofrequencydrive signals may be applied to the acousto-optic device with anysuitable radiofrequency drive signal source, such as a direct digitalsynthesizer (DDS), arbitrary waveform generator (AWG), or electricalpulse generator.

In embodiments, a controller is configured to apply radiofrequency drivesignals to the acousto-optic device to produce the desired number ofangularly deflected laser beams in the output laser beam, such as beingconfigured to apply 3 or more radiofrequency drive signals, such as 4 ormore radiofrequency drive signals, such as 5 or more radiofrequencydrive signals, such as 6 or more radiofrequency drive signals, such as 7or more radiofrequency drive signals, such as 8 or more radiofrequencydrive signals, such as 9 or more radiofrequency drive signals, such as10 or more radiofrequency drive signals, such as 15 or moreradiofrequency drive signals, such as 25 or more radiofrequency drivesignals, such as 50 or more radiofrequency drive signals and includingbeing configured to apply 100 or more radiofrequency drive signals.

In some instances, to produce an intensity profile of the angularlydeflected laser beams in the output laser beam, the controller isconfigured to apply radiofrequency drive signals having an amplitudethat varies such as from about 0.001 V to about 500 V, such as fromabout 0.005 V to about 400 V, such as from about 0.01 V to about 300 V,such as from about 0.05 V to about 200 V, such as from about 0.1 V toabout 100 V, such as from about 0.5 V to about 75 V, such as from about1 V to 50 V, such as from about 2 V to 40 V, such as from 3 V to about30 V and including from about 5 V to about 25 V. Each appliedradiofrequency drive signal has, in some embodiments, a frequency offrom about 0.001 MHz to about 500 MHz, such as from about 0.005 MHz toabout 400 MHz, such as from about 0.01 MHz to about 300 MHz, such asfrom about 0.05 MHz to about 200 MHz, such as from about 0.1 MHz toabout 100 MHz, such as from about 0.5 MHz to about 90 MHz, such as fromabout 1 MHz to about 75 MHz, such as from about 2 MHz to about 70 MHz,such as from about 3 MHz to about 65 MHz, such as from about 4 MHz toabout 60 MHz and including from about 5 MHz to about 50 MHz.

In certain embodiments, the controller has a processor having memoryoperably coupled to the processor such that the memory includesinstructions stored thereon, which when executed by the processor, causethe processor to produce an output laser beam with angularly deflectedlaser beams having a desired intensity profile. For example, the memorymay include instructions to produce two or more angularly deflectedlaser beams with the same intensities, such as 3 or more, such as 4 ormore, such as 5 or more, such as 10 or more, such as 25 or more, such as50 or more and including memory that may include instructions to produce100 or more angularly deflected laser beams with the same intensities.In other embodiments, the memory may include instructions to produce twoor more angularly deflected laser beams with different intensities, suchas 3 or more, such as 4 or more, such as 5 or more, such as 10 or more,such as 25 or more, such as 50 or more and including memory that mayinclude instructions to produce 100 or more angularly deflected laserbeams with different intensities.

In certain embodiments, the controller has a processor having memoryoperably coupled to the processor such that the memory includesinstructions stored thereon, which when executed by the processor, causethe processor to produce an output laser beam having increasingintensity from the edges to the center of the output laser beam alongthe horizontal axis. In these instances, the intensity of the angularlydeflected laser beam at the center of the output beam may range from0.1% to about 99% of the intensity of the angularly deflected laserbeams at the edge of the output laser beam along the horizontal axis,such as from 0.5% to about 95%, such as from 1% to about 90%, such asfrom about 2% to about 85%, such as from about 3% to about 80%, such asfrom about 4% to about 75%, such as from about 5% to about 70%, such asfrom about 6% to about 65%, such as from about 7% to about 60%, such asfrom about 8% to about 55% and including from about 10% to about 50% ofthe intensity of the angularly deflected laser beams at the edge of theoutput laser beam along the horizontal axis. In other embodiments, thecontroller has a processor having memory operably coupled to theprocessor such that the memory includes instructions stored thereon,which when executed by the processor, cause the processor to produce anoutput laser beam having an increasing intensity from the edges to thecenter of the output laser beam along the horizontal axis. In theseinstances, the intensity of the angularly deflected laser beam at theedges of the output beam may range from 0.1% to about 99% of theintensity of the angularly deflected laser beams at the center of theoutput laser beam along the horizontal axis, such as from 0.5% to about95%, such as from 1% to about 90%, such as from about 2% to about 85%,such as from about 3% to about 80%, such as from about 4% to about 75%,such as from about 5% to about 70%, such as from about 6% to about 65%,such as from about 7% to about 60%, such as from about 8% to about 55%and including from about 10% to about 50% of the intensity of theangularly deflected laser beams at the center of the output laser beamalong the horizontal axis. In yet other embodiments, the controller hasa processor having memory operably coupled to the processor such thatthe memory includes instructions stored thereon, which when executed bythe processor, cause the processor to produce an output laser beamhaving an intensity profile with a Gaussian distribution along thehorizontal axis. In still other embodiments, the controller has aprocessor having memory operably coupled to the processor such that thememory includes instructions stored thereon, which when executed by theprocessor, cause the processor to produce an output laser beam having atop hat intensity profile along the horizontal axis.

In embodiments, light beam generators of interest may be configured toproduce angularly deflected laser beams in the output laser beam thatare spatially separated. Depending on the applied radiofrequency drivesignals and desired irradiation profile of the output laser beam, theangularly deflected laser beams may be separated by 0.001 μm or more,such as by 0.005 μm or more, such as by 0.01 μm or more, such as by 0.05μm or more, such as by 0.1 μm or more, such as by 0.5 μm or more, suchas by 1 μm or more, such as by 5 μm or more, such as by 10 μm or more,such as by 100 μm or more, such as by 500 μm or more, such as by 1000 μmor more and including by 5000 μm or more. In some embodiments, systemsare configured to produce angularly deflected laser beams in the outputlaser beam that overlap, such as with an adjacent angularly deflectedlaser beam along a horizontal axis of the output laser beam. The overlapbetween adjacent angularly deflected laser beams (such as overlap ofbeam spots) may be an overlap of 0.001 μm or more, such as an overlap of0.005 μm or more, such as an overlap of 0.01 μm or more, such as anoverlap of 0.05 μm or more, such as an overlap of 0.1 μm or more, suchas an overlap of 0.5 μm or more, such as an overlap of 1 μm or more,such as an overlap of 5 μm or more, such as an overlap of 10 μm or moreand including an overlap of 100 μm or more.

In certain instances, light beam generators configured to generate twoor more beams of frequency shifted light include laser excitationmodules as described in U.S. Pat. Nos. 9,423,353; 9,784,661 and10,006,852 and U.S. Patent Publication Nos. 2017/0133857 and2017/0350803, the disclosures of which are herein incorporated byreference.

In embodiments, systems include a light detection system having a one ormore photodetectors. Photodetectors of interest may include, but are notlimited to optical sensors, such as active-pixel sensors (APSs),avalanche photodiode, image sensors, charge-coupled devices (CCDs),intensified charge-coupled devices (ICCDs), light emitting diodes,photon counters, bolometers, pyroelectric detectors, photoresistors,photovoltaic cells, photodiodes, photomultiplier tubes,phototransistors, quantum dot photoconductors or photodiodes andcombinations thereof, among other photodetectors. In certainembodiments, light from a sample is measured with a charge-coupleddevice (CCD), semiconductor charge-coupled devices (CCD), active pixelsensors (APS), complementary metal-oxide semiconductor (CMOS) imagesensors or N-type metal-oxide semiconductor (NMOS) image sensors.

In some embodiments, light detection systems of interest include aplurality of photodetectors. In some instances, the light detectionsystem includes a plurality of solid-state detectors such asphotodiodes. In certain instances, the light detection system includes aphotodetector array, such as an array of photodiodes. In theseembodiments, the photodetector array may include 4 or morephotodetectors, such as 10 or more photodetectors, such as 25 or morephotodetectors, such as 50 or more photodetectors, such as 100 or morephotodetectors, such as 250 or more photodetectors, such as 500 or morephotodetectors, such as 750 or more photodetectors and including 1000 ormore photodetectors. For example, the detector may be a photodiode arrayhaving 4 or more photodiodes, such as 10 or more photodiodes, such as 25or more photodiodes, such as 50 or more photodiodes, such as 100 or morephotodiodes, such as 250 or more photodiodes, such as 500 or morephotodiodes, such as 750 or more photodiodes and including 1000 or morephotodiodes.

The photodetectors may be arranged in any geometric configuration asdesired, where arrangements of interest include, but are not limited toa square configuration, rectangular configuration, trapezoidalconfiguration, triangular configuration, hexagonal configuration,heptagonal configuration, octagonal configuration, nonagonalconfiguration, decagonal configuration, dodecagonal configuration,circular configuration, oval configuration as well as irregularpatterned configurations. The photodetectors in the photodetector arraymay be oriented with respect to the other (as referenced in an X-Zplane) at an angle ranging from 10° to 180°, such as from 15° to 170°,such as from 20° to 160°, such as from 25° to 150°, such as from 30° to120° and including from 45° to 90°. The photodetector array may be anysuitable shape and may be a rectilinear shape, e.g., squares,rectangles, trapezoids, triangles, hexagons, etc., curvilinear shapes,e.g., circles, ovals, as well as irregular shapes, e.g., a parabolicbottom portion coupled to a planar top portion. In certain embodiments,the photodetector array has a rectangular-shaped active surface.

Each photodetector (e.g., photodiode) in the array may have an activesurface with a width that ranges from 5 μm to 250 μm, such as from 10 μmto 225 μm, such as from 15 μm to 200 μm, such as from 20 μm to 175 μm,such as from 25 μm to 150 μm, such as from 30 μm to 125 μm and includingfrom 50 μm to 100 μm and a length that ranges from 5 μm to 250 μm, suchas from 10 μm to 225 μm, such as from 15 μm to 200 μm, such as from 20μm to 175 μm, such as from 25 μm to 150 μm, such as from 30 μm to 125 μmand including from 50 μm to 100 μm, where the surface area of eachphotodetector (e.g., photodiode) in the array ranges from 25 to μm² to10000 μm², such as from 50 to μm² to 9000 μm², such as from 75 to μm² to8000 μm², such as from 100 to μm² to 7000 μm², such as from 150 to μm²to 6000 μm² and including from 200 to μm² to 5000 μm².

The size of the photodetector array may vary depending on the amount andintensity of the light, the number of photodetectors and the desiredsensitivity and may have a length that ranges from 0.01 mm to 100 mm,such as from 0.05 mm to 90 mm, such as from 0.1 mm to 80 mm, such asfrom 0.5 mm to 70 mm, such as from 1 mm to 60 mm, such as from 2 mm to50 mm, such as from 3 mm to 40 mm, such as from 4 mm to 30 mm andincluding from 5 mm to 25 mm. The width of the photodetector array mayalso vary, ranging from 0.01 mm to 100 mm, such as from 0.05 mm to 90mm, such as from 0.1 mm to 80 mm, such as from 0.5 mm to 70 mm, such asfrom 1 mm to 60 mm, such as from 2 mm to 50 mm, such as from 3 mm to 40mm, such as from 4 mm to 30 mm and including from 5 mm to 25 mm. Assuch, the active surface of the photodetector array may range from 0.1mm² to 10000 mm², such as from 0.5 mm² to 5000 mm², such as from 1 mm²to 1000 mm², such as from 5 mm² to 500 mm², and including from 10 mm² to100 mm².

Photodetectors of interest are configured to measure collected light atone or more wavelengths, such as at 2 or more wavelengths, such as at 5or more different wavelengths, such as at 10 or more differentwavelengths, such as at 25 or more different wavelengths, such as at 50or more different wavelengths, such as at 100 or more differentwavelengths, such as at 200 or more different wavelengths, such as at300 or more different wavelengths and including measuring light emittedby a sample in the flow stream at 400 or more different wavelengths.

In some embodiments, photodetectors are configured to measure collectedlight over a range of wavelengths (e.g., 200 nm-1000 nm). In certainembodiments, photodetectors of interest are configured to collectspectra of light over a range of wavelengths. For example, systems mayinclude one or more detectors configured to collect spectra of lightover one or more of the wavelength ranges of 200 nm-1000 nm. In yetother embodiments, detectors of interest are configured to measure lightfrom the sample in the flow stream at one or more specific wavelengths.For example, systems may include one or more detectors configured tomeasure light at one or more of 450 nm, 518 nm, 519 nm, 561 nm, 578 nm,605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm,710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm and any combinationsthereof. In certain embodiments, photodetectors may be configured to bepaired with specific fluorophores, such as those used with the sample ina fluorescence assay. In some embodiments, photodetectors are configuredto measure collected light across the entire fluorescence spectrum ofeach fluorophore in the sample.

The light detection system is configured to measure light continuouslyor in discrete intervals. In some instances, photodetectors of interestare configured to take measurements of the collected light continuously.In other instances, the light detection system is configured to takemeasurements in discrete intervals, such as measuring light every 0.001millisecond, every 0.01 millisecond, every 0.1 millisecond, every 1millisecond, every 10 milliseconds, every 100 milliseconds and includingevery 1000 milliseconds, or some other interval.

In embodiments, systems include a cell sorter configured to receive thesample comprising cells flowing in the flow stream. By cell sorter, itis meant any convenient module for sorting particles, such as cells,from the flow stream, as described below. As described below, the term“sorting” is used herein in its conventional sense to refer toseparating components (e.g., cells, non-cellular particles such asbiological macromolecules) of the sample and in some instancesdelivering the separated components to one or more partitions, such assample collection containers.

In embodiments, systems include a plurality of partitions configured toreceive cells from the sample sorted by the cell sorter. By partition,it is meant any convenient container, such as a sample collectioncontainer, capable of receiving one or more particles, such as a cell,sorted by the cell sorter and maintaining the contents of the partitionseparate and isolated from other materials not sorted into thepartition. Embodiments include more than one partition, such as twopartitions, four partitions, 96 partitions or 1536 or more partitions.Partitions may be any convenient size that is capable of receiving andmaintaining particles, such as cells, isolated from the sample of theflow stream. In some cases, partitions are sized to hold more than onecell, such as 10 cells, 100 cells, 1000 cells, 10000 cells or more. Insome embodiments, partitions comprise wells. In instances, wells may besmall test tubes. Wells may be any convenient shape. In some instances,the shape of the lateral cross section of wells is circular; in othercases, it is rectangular or square. Wells may be any size withsufficient capacity for holding particles, such as cells, as needed. Forexample, the volume of a well may be 0.001 mL or greater, such as 0.005mL or 0.015 mL or 0.1 mL or 2 mL or 5 mL or greater. In someembodiments, wells may be wells of a multi-well plate. A multi-wellplate may include any number of wells. In instances, a multi-well platemay include six or 12 or 24 or 48 or 96 or 384 or 1536 or 3456 or 9600or more wells. Wells of a multi-well plate may be arranged in anyconvenient pattern. In some instances, wells are arranged in arectangular shape with a length to width ration of approximately two tothree. In some instances, multi-well plates of the present disclosuremay conform to accepted standards such as a standard established by theSociety for Biomolecular Sciences with the ANSI-Standards. Multi-wellplates may be composed of any convenient material. In some cases,multi-well plates may be composed of polypropylene, polystyrene orpolycarbonate. In these embodiments, the multi-well plate may beadvanced to a second well after sorting a predetermined number of cellsinto a first well. The predetermined number of cells may be one cell,two cells, ten cells or 100 cells or more.

Systems of the present disclosure are configured to flow cytometricallysort a sample with particles, such as cells. In embodiments, systems areconfigured to identify phenotypes of cells in the sample based on one ormore data signals generated from detected light. Light detection systemsare configured to generate a plurality of data signals from the lightdetected from particles, such as cells, in the sample. The data signalsgenerated may be analog data signals or digital data signals. Where thedata signals are analog data signals, in some instances, systems furtherinclude an analog-to-digital converter configured to convert the analogdata signals to digital data signals. Systems of interest include aprocessor having memory operably coupled to the processor where thememory includes instructions stored thereon which when executed by theprocessor cause the processor to identify phenotypes of cells in thesample based on one or more of the data signals generated from detectedlight. In some instances, identifying a cell phenotype includesassigning the cell to a cell population cluster. In other instances,identifying a cell phenotype includes plotting one or more data signalsgenerated from detected light of the cell onto a scatter plot. Incertain instances, identifying cell phenotypes in the sample includesgenerating a two-dimensional bitmap having a region of interest (ROI)and determining whether a particle should be assigned to the ROI of thebitmap. In systems of the present disclosure memory also includesinstructions stored thereon which when executed by the processor causethe processor to instruct the cell sorter to dynamically sort into thepartitions cells of the sample that have a phenotype of a collection ofpredetermined phenotypes based on order of identification. As describedabove, by “sorting based on order of identification,” it is meant, forexample, sorting cells in the order in which such cells appear in theflow stream. By sorting cells with phenotypes belonging to apredetermined collection of phenotypes, it is meant that, for example,in some embodiments, instructions stored on the memory, when executed bythe processor, cause the processor to compare an identified cellphenotype with the one or more predetermined cell phenotypes of thepredetermined collection of cell phenotypes, generating a true or falseresult indicating whether the identified cell phenotype is identical tothe one or more phenotypes belonging to the predetermined collection ofphenotypes. By “instructing the cell sorter,” it is meant that theprocessor causes the cell sorter to sort a cell, the phenotype of whichhas been identified. The processor may instruct the cell sorter by anyconvenient means, for example, in some embodiments by conveyingelectrical signals encoding a sort instruction via an operableconnection between the processor and the cell sorter. Such an operableconnection may be a wired or wireless connection.

In some embodiments, the memory includes instructions stored thereon,which, when executed by the processor, cause the processor to instructthe cell sorter to stop sorting a first phenotype from the collection ofphenotypes after a predetermined number of cells that have the firstphenotype are sorted. By stop sorting cells that have a first phenotype,it is meant that upon execution of such instructions by the processor,in some embodiments, when a cell is identified as having a firstphenotype, the processor instructs the cell sorter not to sort suchcell. In some instances, instructing the cell sorter not to sort a cellmay be accomplished by omitting to issue an affirmative sortinstruction. In some embodiments, the memory includes instructionsstored thereon, which, when executed by the processor, cause theprocessor to instruct the cell sorter to dynamically sort apredetermined number of cells into a partition. In some instances, thepredetermined number of cells may be one cell, two cells, ten cells, 100cells or more. For example, the predetermined number of cells may beone.

In some embodiments, the system further comprises a translatable supportstage configured to move the multi-well plate, and the processorcomprises memory operably coupled to the processor, wherein the memorycomprises instructions stored thereon, which, when executed by theprocessor, cause the processor to instruct the support stage to move themulti-well plate to a second well after sorting a predetermined numberof cells into a first well. By translatable support stage, it is meantany convenient stage capable of receiving a multi-well plate. Anyconvenient displacement protocol may be employed to translate thesupport stage, such as moving the support stage with a motor actuatedtranslation stage, leadscrew translation assembly, geared translationdevice, such as those employing a stepper motor, servo motor, brushlesselectric motor, brushed DC motor, micro-step drive motor, highresolution stepper motor, among other types of motors.

In embodiments, the memory includes instructions stored thereon whichwhen executed by the processor cause the processor to store thephenotypes of cells sorted into partitions. By store phenotypes ofsorted cells, it is meant that summary representations of cellphenotypes in any convenient data format are added to a memory, which insome cases may be a dedicated hardware memory. In embodiments, thememory includes instructions stored thereon which when executed by theprocessor cause the processor to store the phenotypes of cells sortedinto partitions by adding representations of phenotypes of sorted cellsto a list of phenotypes. In some cases, by list of phenotypes, it ismeant, for example, an array allocated in memory capable of receivingsummary representations of cell phenotypes. In other cases, it is meanta linked list data structure in memory capable of receiving summaryrepresentations of cell phenotypes. In some instances, the linked listmay be sorted. In other cases, it is meant a binary tree data structureallocated in memory, capable of receiving summary representations ofcell phenotypes. In these embodiments, the memory may compriseinstructions stored thereon, which, when executed by the processor,cause the processor to query the list of phenotypes to determine whetheror not a first cell has already been sorted, wherein the first cell hasa phenotype also exhibited by a second cell. Any convenient search andcomparison routines or algorithms may be employed to determine whetheror not a cell phenotype is already represented in the list of phenotypesand thus has already been sorted.

In embodiments, the memory includes instructions stored thereon whichwhen executed by the processor cause the processor to store thephenotypes of cells sorted into partitions by adding representations ofphenotypes of sorted cells to a probabilistic data structure. Asdescribed above, a probabilistic data structure is a data structurethat, in some cases, may be capable of returning only probabilistic andnot definitive information about the properties of the data structure,such as whether the data structure includes a query term, such as arepresentation of a cell phenotype. In these embodiments, the memory maycomprise instructions stored thereon, which, when executed by theprocessor, cause the processor to query the probabilistic data structureto determine whether or not a first cell has already been sorted,wherein the first cell has a phenotype also exhibited by a second cell.In some cases, by querying the probabilistic data structure, theprobabilistic data structure may be capable of indicating only aprobabilistic result, such as a cell phenotype has likely, but has notdefinitively, already been sorted. In some cases, the probabilistic datastructure is a Bloom filter, as described above.

In embodiments, the memory includes instructions stored thereon whichwhen executed by the processor cause the processor to store thephenotypes of cells sorted into partitions by adding representations ofphenotypes of sorted cells to an associative array, as described above.In these embodiments, the memory may comprise instructions storedthereon, which, when executed by the processor, cause the processor toquery the associative array to determine whether or not a first cell hasalready been sorted, wherein the first cell has a phenotype alsoexhibited by a second cell. In some cases, the associative array is acontent addressable memory. Any convenient hardware or software searchand comparison routines and algorithms may be employed to query theassociative array to determine whether or not a first cell has alreadybeen sorted.

In embodiments, the memory includes instructions stored thereon whichwhen executed by the processor cause the processor to store thephenotypes of cells sorted into partitions by setting a value at alocation in a truth table corresponding to a cell phenotype. Ininstances, the truth table may comprise a dedicated hardware module. Inthese embodiments, the memory may comprise instructions stored thereon,which, when executed by the processor, cause the processor to query thetruth table to determine whether or not a first cell has already beensorted, wherein the first cell has a phenotype also exhibited by asecond cell. The contents of the truth table may be Boolean values, eachrepresented by one or more bits.

In embodiments, the memory includes instructions stored thereon whichwhen executed by the processor cause the processor to store informationidentifying a partition and a phenotype of a cell sorted into thepartition. As described above, any convenient summary identification ofthe location of a partition, such as, in some cases, the horizontal andvertical coordinates of a well in a multi-well plate, may be employed toidentify a partition. In instances, a cell phenotype may be representedas a bit string in which each bit of the bit string corresponds to thepresence or absence of a constituent characteristic of a cell phenotype.In these embodiments, the memory may comprise instructions storedthereon, which, when executed by the processor, cause the processor tomaintain a list identifying partitions and phenotypes of cells sortedinto the partitions. The list may take any convenient form, such as anarray, a linked list, or a binary search tree. Representations ofpartitions and phenotypes of cells may take the form of, for example,bit strings, and may be stored in memory, including in a dedicatedmemory module.

In embodiments, the memory includes instructions stored thereon whichwhen executed by the processor cause the processor to instruct the cellsorter to: dynamically sort into a first partition cells of the samplethat exhibit a first collection of phenotypes, comprising one or morephenotypes, based on order of identification, and dynamically sort intoa second partition cells of the sample that do not exhibit the firstcollection of phenotypes. In these embodiments, the memory may compriseinstructions stored thereon, which, when executed by the processor,cause the processor to dynamically update the first collection ofphenotypes based on sorted cells in the sample so that cells sorted intothe first partition and cells sorted into the second partition comprisesubstantially the same number of cells.

In embodiments, the memory includes instructions stored thereon whichwhen executed by the processor cause the processor to determine one ormore parameters of a particle in the flow stream from the generated datasignals. In embodiments, the memory includes instructions stored thereonwhich when executed by the processor cause the processor to not sort acell exhibiting an indeterminate phenotype. By not sorting a cell, insome instances, it is meant that the processor omits sending a sortinstruction to the cell sorter. By indeterminate phenotype, it is meant,as described above, a phenotype wherein a particular phenotype, or, insome cases, a constituent characteristic of a cell phenotype, canneither be definitively ruled in nor ruled out as present in a cell.

In embodiments, the memory includes instructions stored thereon whichwhen executed by the processor cause the processor to calculate anestimate of an amount of fluorescent spillover measured with respect toa single data signal based on a predicted variance-covariance matrix ofunmixed data for a sorted cell. In these embodiments, the memory maycomprise instructions stored thereon, which, when executed by theprocessor, cause the processor to calculate an estimate of a covariancebetween two fluorophores based on the predicted variance-covariancematrix of unmixed data for the sorted cell. In these embodiments, thememory may comprise instructions stored thereon, which, when executed bythe processor, cause the processor to calculate thresholds forexpression for a fluorophore based on the estimated covariance betweenfluorophores. In these embodiments, the memory may comprise instructionsstored thereon, which, when executed by the processor, cause theprocessor to calculate a measure of uncertainty with respect to thephenotype exhibited by a sorted cell based on the estimated covariancebetween fluorophores.

In some embodiments, systems of interest may include one or more sortdecision modules configured to generate a sorting decision for theparticle, such as the cell, based on identifying the phenotype of thecell and determining whether the cell phenotype belongs to thecollection of predetermined phenotypes. As described above, systemsfurther include a particle sorter, that is a cell sorter (e.g., having adroplet deflector), for sorting the particles, such as cells, from theflow stream based on the sort decision generated by the sort decisionmodule. As described above, the term “sorting” is used herein in itsconventional sense to refer to separating components (e.g., cells,non-cellular particles such as biological macromolecules) of the sampleand in some instances delivering the separated components to one or morepartitions, such as sample collection containers. For example, thesubject systems may be configured for sorting samples having 2 or morecomponents, such as 3 or more components, such as 4 or more components,such as 5 or more components, such as 10 or more components, such as 15or more components and including sorting a sample having 25 or morecomponents. One or more of the sample components may be separated fromthe sample and delivered to a sample collection container, such as 2 ormore sample components, such as 3 or more sample components, such as 4or more sample components, such as 5 or more sample components, such as10 or more sample components and including 15 or more sample componentsmay be separated from the sample and delivered to a sample collectioncontainer. In some cases, the phrase “sample components” refers to cellswith differing cell phenotypes.

In some embodiments, particle sorting systems of interest are configuredto sort particles, such as cells, with an enclosed particle sortingmodule (i.e., cell sorter), such as those described in U.S. PatentPublication No. 2017/0299493, filed on Mar. 28, 2017, the disclosure ofwhich is incorporated herein by reference. In certain embodiments,particles (e.g., cells) of the sample are sorted using a sort decisionmodule having a plurality of sort decision units, such as thosedescribed in U.S. Provisional Patent Application No. 62/803,264, filedon Feb. 8, 2019, the disclosure of which is incorporated herein byreference. In some embodiments, methods for sorting components of sampleinclude sorting particles (e.g., cells in a biological sample) with aparticle sorting module having deflector plates, such as described inU.S. Patent Publication No. 2017/0299493, filed on Mar. 28, 2017, thedisclosure of which is incorporated herein by reference.

FIG. 1 shows a functional block diagram for one example of a sortingcontrol system, such as an analytics controller 100, for analyzing anddisplaying biological events. An analytics controller 100 can beconfigured to implement a variety of processes for controlling graphicdisplay of biological events.

A particle analyzer or sorting system 102 can be configured to acquirebiological event data. For example, a flow cytometer can generate flowcytometric event data. The particle analyzer 102 can be configured toprovide biological event data to the analytics controller 100. A datacommunication channel can be included between the particle analyzer 102and the analytics controller 100. The biological event data can beprovided to the analytics controller 100 via the data communicationchannel.

The analytics controller 100 can be configured to receive biologicalevent data from the particle analyzer 102. The biological event datareceived from the particle analyzer 102 can include flow cytometricevent data. The analytics controller 100 can be configured to provide agraphical display including a first plot of biological event data to adisplay device 106. The analytics controller 100 can be furtherconfigured to render a region of interest as a gate around a populationof biological event data shown by the display device 106, overlaid uponthe first plot, for example. In some embodiments, the gate can be alogical combination of one or more graphical regions of interest drawnupon a single parameter histogram or bivariate plot. In someembodiments, the display can be used to display particle parameters.

The analytics controller 100 can be further configured to display thebiological event data on the display device 106 within the gatedifferently from other events in the biological event data outside ofthe gate. For example, the analytics controller 100 can be configured torender the color of biological event data contained within the gate tobe distinct from the color of biological event data outside of the gate.The display device 106 can be implemented as a monitor, a tabletcomputer, a smartphone, or other electronic device configured to presentgraphical interfaces.

The analytics controller 100 can be configured to receive a gateselection signal identifying the gate from a first input device. Forexample, the first input device can be implemented as a mouse 110. Themouse 110 can initiate a gate selection signal to the analyticscontroller 100 identifying the gate to be displayed on or manipulatedvia the display device 106 (e.g., by clicking on or in the desired gatewhen the cursor is positioned there). In some implementations, the firstdevice can be implemented as the keyboard 108 or other means forproviding an input signal to the analytics controller 100 such as atouchscreen, a stylus, an optical detector, or a voice recognitionsystem. Some input devices can include multiple inputting functions. Insuch implementations, the inputting functions can each be considered aninput device. For example, as shown in FIG. 1, the mouse 110 can includea right mouse button and a left mouse button, each of which can generatea triggering event.

The triggering event can cause the analytics controller 100 to alter themanner in which the data is displayed, which portions of the data isactually displayed on the display device 106, and/or provide input tofurther processing such as selection of a population of interest forparticle sorting.

In some embodiments, the analytics controller 100 can be configured todetect when gate selection is initiated by the mouse 110. The analyticscontroller 100 can be further configured to automatically modify plotvisualization to facilitate the gating process. The modification can bebased on the specific distribution of biological event data received bythe analytics controller 100.

The analytics controller 100 can be connected to a storage device 104.The storage device 104 can be configured to receive and store biologicalevent data from the analytics controller 100. The storage device 104 canalso be configured to receive and store flow cytometric event data fromthe analytics controller 100. The storage device 104 can be furtherconfigured to allow retrieval of biological event data, such as flowcytometric event data, by the analytics controller 100.

A display device 106 can be configured to receive display data from theanalytics controller 100. The display data can comprise plots ofbiological event data and gates outlining sections of the plots. Thedisplay device 106 can be further configured to alter the informationpresented according to input received from the analytics controller 100in conjunction with input from the particle analyzer 102, the storagedevice 104, the keyboard 108, and/or the mouse 110.

In some implementations the analytics controller 100 can generate a userinterface to receive example events for sorting. For example, the userinterface can include a control for receiving example events or exampleimages. The example events or images or an example gate can be providedprior to collection of event data for a sample or based on an initialset of events for a portion of the sample.

FIG. 2A is a schematic drawing of a particle sorter system 200 (e.g.,the particle analyzer 102) in accordance with one embodiment presentedherein. In some embodiments, the particle sorter system 200 is a cellsorter system. As shown in FIG. 2A, a drop formation transducer 202(e.g., piezo-oscillator) is coupled to a fluid conduit 201, which can becoupled to, can include, or can be, a nozzle 203. Within the fluidconduit 201, sheath fluid 204 hydrodynamically focuses a sample fluid206 comprising particles 209 into a moving fluid column 208 (e.g., astream). Within the moving fluid column 208, particles 209 (e.g., cells)are lined up in single file to cross a monitored area 211 (e.g., wherelaser-stream intersect), irradiated by an irradiation source 212 (e.g.,a laser). Vibration of the drop formation transducer 202 causes movingfluid column 208 to break into a plurality of drops 210, some of whichcontain particles 209.

In operation, a detection station 214 (e.g., an event detector)identifies when a particle of interest (or cell of interest) crosses themonitored area 211. Detection station 214 feeds into a timing circuit228, which in turn feeds into a flash charge circuit 230. At a dropbreak off point, informed by a timed drop delay (Δt), a flash charge canbe applied to the moving fluid column 208 such that a drop of interestcarries a charge. The drop of interest can include one or more particlesor cells to be sorted. The charged drop can then be sorted by activatingdeflection plates (not shown) to deflect the drop into partitions, forexample, a vessel such as a collection tube or a multi-well or microwellsample plate where a partition or a well or a microwell can beassociated with drops of particular interest. As shown in FIG. 2A, thedrops can be collected in a drain receptacle 238.

A detection system 216 (e.g., a drop boundary detector) serves toautomatically determine the phase of a drop drive signal when a particleof interest passes the monitored area 211. An exemplary drop boundarydetector is described in U.S. Pat. No. 7,679,039, which is incorporatedherein by reference in its entirety. The detection system 216 allows theinstrument to accurately calculate the place of each detected particlein a drop. The detection system 216 can feed into an amplitude signal220 and/or phase 218 signal, which in turn feeds (via amplifier 222)into an amplitude control circuit 226 and/or frequency control circuit224. The amplitude control circuit 226 and/or frequency control circuit224, in turn, controls the drop formation transducer 202. The amplitudecontrol circuit 226 and/or frequency control circuit 224 can be includedin a control system.

In some implementations, sort electronics (e.g., the detection system216, the detection station 214 and a processor 240) can be coupled witha memory configured to store the detected events and a sort decisionbased thereon. The sort decision can be included in the event data for aparticle. In some implementations, the detection system 216 and thedetection station 214 can be implemented as a single detection unit orcommunicatively coupled such that an event measurement can be collectedby one of the detection system 216 or the detection station 214 andprovided to the non-collecting element.

FIG. 2B is a schematic drawing of a particle sorter system, inaccordance with one embodiment presented herein. The particle sortersystem 200 shown in FIG. 2B includes deflection plates 252 and 254. Acharge can be applied via a stream-charging wire in a barb. This createsa stream of droplets 210 containing particles 210 for analysis. Theparticles can be illuminated with one or more light sources (e.g.,lasers) to generate light scatter and fluorescence information. Theinformation for a particle is analyzed such as by sorting electronics orother detection system (not shown in FIG. 2B). The deflection plates 252and 254 can be independently controlled to attract or repel the chargeddroplet to guide the droplet toward a destination collection receptacle(e.g., one of 272, 274, 276, or 278), such as a partition. As shown inFIG. 2B, the deflection plates 252 and 254 can be controlled to direct aparticle along a first path 262 toward the receptacle 274 or along asecond path 268 toward the receptacle 278. If the particle is not ofinterest (e.g., does not exhibit scatter or illumination informationwithin a specified sort range), deflection plates may allow the particleto continue along a flow path 264. Such uncharged droplets may pass intoa waste receptacle such as via aspirator 270.

The sorting electronics can be included to initiate collection ofmeasurements, receive fluorescence signals for particles, and determinehow to adjust the deflection plates to cause sorting of the particles.Example implementations of the embodiment shown in FIG. 2B include theBD FACSAria™ line of flow cytometers commercially provided by Becton,Dickinson and Company (Franklin Lakes, N.J.).

In some embodiments, one or more components described for the particlesorter system 200 can be used to analyze and characterize particles,with or without physically sorting the particles into collectionvessels. Likewise, one or more components described below for theparticle analysis system 300 (FIG. 3) can be used to analyze andcharacterize particles, with or without physically sorting the particlesinto collection vessels. For example, particles can be grouped ordisplayed in a tree that includes at least three groups as describedherein, using one or more of the components of the particle sortersystem 200 or particle analysis system 300.

FIG. 3 shows a functional block diagram of a particle analysis systemfor computational based sample analysis and particle characterization.In some embodiments, the particle analysis system 300 is a flow system.The particle analysis system 300 shown in FIG. 3 can be configured toperform, in whole or in part, the methods described herein. The particleanalysis system 300 includes a fluidics system 302. The fluidics system302 can include or be coupled with a sample tube 310 and a moving fluidcolumn within the sample tube in which particles 330 (e.g., cells) of asample move along a common sample path 320.

The particle analysis system 300 includes a detection system 304configured to collect a signal from each particle as it passes one ormore detection stations along the common sample path. A detectionstation 308 generally refers to a monitored area 340 of the commonsample path. Detection can, in some implementations, include detectinglight or one or more other properties of the particles 330 as they passthrough a monitored area 340. In FIG. 3, one detection station 308 withone monitored area 340 is shown. Some implementations of the particleanalysis system 300 can include multiple detection stations.Furthermore, some detection stations can monitor more than one area.

Each signal is assigned a signal value to form a data point for eachparticle. As described above, this data can be referred to as eventdata. The data point can be a multidimensional data point includingvalues for respective properties measured for a particle. The detectionsystem 304 is configured to collect a succession of such data points ina first time interval.

The particle analysis system 300 can also include a control system 306.The control system 306 can include one or more processors, an amplitudecontrol circuit 226 and/or a frequency control circuit 224 as shown inFIG. 2A. The control system 206 shown can be operationally associatedwith the fluidics system 302. The control system 306 can be configuredto generate a calculated signal frequency for at least a portion of thefirst time interval based on a Poisson distribution and the number ofdata points collected by the detection system 304 during the first timeinterval. The control system 306 can be further configured to generatean experimental signal frequency based on the number of data points inthe portion of the first time interval. The control system 306 canadditionally compare the experimental signal frequency with that of acalculated signal frequency or a predetermined signal frequency.

FIG. 4 shows a system 400 for flow cytometry in accordance with anillustrative embodiment of the present invention. The system 400includes a flow cytometer 410, a controller/processor 490 and a memory495. The flow cytometer 410 includes one or more excitation lasers 415a-415 c, a focusing lens 420, a flow chamber 425, a forward scatterdetector 430, a side scatter detector 435, a fluorescence collectionlens 440, one or more beam splitters 445 a-445 g, one or more bandpassfilters 450 a-450 e, one or more longpass (“LP”) filters 455 a-455 b,and one or more fluorescent detectors 460 a-460 f.

The excitation lasers 415 a-c emit light in the form of a laser beam.The wavelengths of the laser beams emitted from excitation lasers 415a-415 c are 488 nm, 633 nm, and 325 nm, respectively, in the examplesystem of FIG. 4. The laser beams are first directed through one or moreof beam splitters 445 a and 445 b. Beam splitter 445 a transmits lightat 488 nm and reflects light at 633 nm. Beam splitter 445 b transmits UVlight (light with a wavelength in the range of 10 to 400 nm) andreflects light at 488 nm and 633 nm.

The laser beams are then directed to a focusing lens 420, which focusesthe beams onto the portion of a fluid stream where particles of a sampleare located, within the flow chamber 425. The flow chamber is part of afluidics system which directs particles, typically one at a time, in astream to the focused laser beam for interrogation. The flow chamber cancomprise a flow cell in a benchtop cytometer or a nozzle tip in astream-in-air cytometer.

The light from the laser beam(s) interacts with the particles in thesample by diffraction, refraction, reflection, scattering, andabsorption with re-emission at various different wavelengths dependingon the characteristics of the particle such as its size, internalstructure, and the presence of one or more fluorescent moleculesattached to or naturally present on or in the particle. The fluorescenceemissions as well as the diffracted light, refracted light, reflectedlight, and scattered light may be routed to one or more of the forwardscatter detector 430, the side scatter detector 435, and the one or morefluorescent detectors 460 a-460 f through one or more of the beamsplitters 445 a-445 g, the bandpass filters 450 a-450 e, the longpassfilters 455 a-455 b, and the fluorescence collection lens 440.

The fluorescence collection lens 440 collects light emitted from theparticle-laser beam interaction and routes that light towards one ormore beam splitters and filters. Bandpass filters, such as bandpassfilters 450 a-450 e, allow a narrow range of wavelengths to pass throughthe filter. For example, bandpass filter 450 a is a 510/20 filter. Thefirst number represents the center of a spectral band. The second numberprovides a range of the spectral band. Thus, a 510/20 filter extends 10nm on each side of the center of the spectral band, or from 500 nm to520 nm. Shortpass filters transmit wavelengths of light equal to orshorter than a specified wavelength. Longpass filters, such as longpassfilters 455 a-455 b, transmit wavelengths of light equal to or longerthan a specified wavelength of light. For example, longpass filter 455a, which is a 670 nm longpass filter, transmits light equal to or longerthan 670 nm. Filters are often selected to optimize the specificity of adetector for a particular fluorescent dye. The filters can be configuredso that the spectral band of light transmitted to the detector is closeto the emission peak of a fluorescent dye.

Beam splitters direct light of different wavelengths in differentdirections. Beam splitters can be characterized by filter propertiessuch as shortpass and longpass. For example, beam splitter 445 g is a620 SP beam splitter, meaning that the beam splitter 445 g transmitswavelengths of light that are 620 nm or shorter and reflects wavelengthsof light that are longer than 620 nm in a different direction. In oneembodiment, the beam splitters 445 a-445 g can comprise optical mirrors,such as dichroic mirrors.

The forward scatter detector 430 is positioned slightly off axis fromthe direct beam through the flow cell and is configured to detectdiffracted light, the excitation light that travels through or aroundthe particle in mostly a forward direction. The intensity of the lightdetected by the forward scatter detector is dependent on the overallsize of the particle. The forward scatter detector can include aphotodiode. The side scatter detector 435 is configured to detectrefracted and reflected light from the surfaces and internal structuresof the particle and tends to increase with increasing particlecomplexity of structure. The fluorescence emissions from fluorescentmolecules associated with the particle can be detected by the one ormore fluorescent detectors 460 a-460 f. The side scatter detector 435and fluorescent detectors can include photomultiplier tubes. The signalsdetected at the forward scatter detector 430, the side scatter detector435 and the fluorescent detectors can be converted to electronic signals(voltages) by the detectors. This data can provide information about thesample.

One of skill in the art will recognize that a flow cytometer inaccordance with an embodiment of the present invention is not limited tothe flow cytometer depicted in FIG. 4, but can include any flowcytometer known in the art. For example, a flow cytometer may have anynumber of lasers, beam splitters, filters, and detectors at variouswavelengths and in various different configurations.

In operation, cytometer operation is controlled by acontroller/processor 490, and the measurement data from the detectorscan be stored in the memory 495 and processed by thecontroller/processor 490. Although not shown explicitly, thecontroller/processor 190 is coupled to the detectors to receive theoutput signals therefrom and may also be coupled to electrical andelectromechanical components of the flow cytometer 400 to control thelasers, fluid flow parameters, and the like. Input/output (I/O)capabilities 497 may be provided also in the system. The memory 495,controller/processor 490, and I/O 497 may be entirely provided as anintegral part of the flow cytometer 410. In such an embodiment, adisplay may also form part of the I/O capabilities 497 for presentingexperimental data to users of the cytometer 400. Alternatively, some orall of the memory 495 and controller/processor 490 and I/O capabilitiesmay be part of one or more external devices such as a general purposecomputer. In some embodiments, some or all of the memory 495 andcontroller/processor 490 can be in wireless or wired communication withthe cytometer 410. The controller/processor 490 in conjunction with thememory 495 and the I/O 497 can be configured to perform variousfunctions related to the preparation and analysis of a flow cytometerexperiment.

The system illustrated in FIG. 4 includes six different detectors thatdetect fluorescent light in six different wavelength bands (which may bereferred to herein as a “filter window” for a given detector) as definedby the configuration of filters and/or splitters in the beam path fromthe flow cell 425 to each detector. Different fluorescent molecules usedfor a flow cytometer experiment will emit light in their owncharacteristic wavelength bands. The particular fluorescent labels usedfor an experiment and their associated fluorescent emission bands may beselected to generally coincide with the filter windows of the detectors.However, as more detectors are provided, and more labels are utilized,perfect correspondence between filter windows and fluorescent emissionspectra is not possible. It is generally true that although the peak ofthe emission spectra of a particular fluorescent molecule may lie withinthe filter window of one particular detector, some of the emissionspectra of that label will also overlap the filter windows of one ormore other detectors. This may be referred to as spillover. The I/O 497can be configured to receive data regarding a flow cytometer experimenthaving a panel of fluorescent labels and a plurality of cell populationshaving a plurality of markers, each cell population having a subset ofthe plurality of markers. The I/O 497 can also be configured to receivebiological data assigning one or more markers to one or more cellpopulations, marker density data, emission spectrum data, data assigninglabels to one or more markers, and cytometer configuration data. Flowcytometer experiment data, such as label spectral characteristics andflow cytometer configuration data can also be stored in the memory 495.The controller/processor 490 can be configured to evaluate one or moreassignments of labels to markers.

Systems according to some embodiments, may include a display andoperator input device. Operator input devices may, for example, be akeyboard, mouse, or the like. The processing module includes a processorwhich has access to a memory having instructions stored thereon forperforming the steps of the subject methods. The processing module mayinclude an operating system, a graphical user interface (GUI)controller, a system memory, memory storage devices, and input-outputcontrollers, cache memory, a data backup unit, and many other devices.The processor may be a commercially available processor, or it may beone of other processors that are or will become available. The processorexecutes the operating system, and the operating system interfaces withfirmware and hardware in a well-known manner and facilitates theprocessor in coordinating and executing the functions of variouscomputer programs that may be written in a variety of programminglanguages, such as Java, Perl, C++, other high level or low-levellanguages, as well as combinations thereof, as is known in the art. Theoperating system, typically in cooperation with the processor,coordinates and executes functions of the other components of thecomputer. The operating system also provides scheduling, input-outputcontrol, file and data management, memory management, and communicationcontrol and related services, all in accordance with known techniques.The processor may be any suitable analog or digital system. In someembodiments, the processor includes analog electronics which providefeedback control, such as for example negative feedback control.

The system memory may be any of a variety of known or future memorystorage devices. Examples include any commonly available random-accessmemory (RAM), magnetic medium such as a resident hard disk or tape, anoptical medium such as a read and write compact disc, flash memorydevices, or other memory storage device. The memory storage device maybe any of a variety of known or future devices, including a compact diskdrive, a tape drive, a removable hard disk drive, or a diskette drive.Such types of memory storage devices typically read from, and/or writeto, a program storage medium (not shown) such as, respectively, acompact disk, magnetic tape, removable hard disk, or floppy diskette.Any of these program storage media, or others now in use or that maylater be developed, may be considered a computer program product. Aswill be appreciated, these program storage media typically store acomputer software program and/or data. Computer software programs, alsocalled computer control logic, typically are stored in system memoryand/or the program storage device used in conjunction with the memorystorage device.

In some embodiments, a computer program product is described comprisinga computer usable medium having control logic (computer softwareprogram, including program code) stored therein. The control logic, whenexecuted by the processor of the computer, causes the processor toperform functions described herein. In other embodiments, some functionsare implemented primarily in hardware using, for example, a hardwarestate machine. Implementation of the hardware state machine so as toperform the functions described herein will be apparent to those skilledin the relevant arts.

Memory may be any suitable device in which the processor can store andretrieve data, such as magnetic, optical, or solid-state storage devices(including magnetic or optical disks or tape or RAM, or any othersuitable device, either fixed or portable). The processor may include ageneral-purpose digital microprocessor suitably programmed from acomputer readable medium carrying necessary program code. Programmingcan be provided remotely to the processor through a communicationchannel, or previously saved in a computer program product such asmemory or some other portable or fixed computer readable storage mediumusing any of those devices in connection with memory. For example, amagnetic or optical disk may carry the programming, and can be read by adisk writer/reader. Systems of the invention also include programming,e.g., in the form of computer program products, algorithms for use inpracticing the methods as described above. Programming according to thepresent invention can be recorded on computer readable media, e.g., anymedium that can be read and accessed directly by a computer. Such mediainclude, but are not limited to: magnetic storage media, such as floppydiscs, hard disc storage medium, and magnetic tape; optical storagemedia such as CD-ROM; electrical storage media such as RAM and ROM;portable flash drive; and hybrids of these categories such asmagnetic/optical storage media.

The processor may also have access to a communication channel tocommunicate with a user at a remote location. By remote location ismeant the user is not directly in contact with the system and relaysinput information to an input manager from an external device, such as acomputer connected to a Wide Area Network (“WAN”), telephone network,satellite network, or any other suitable communication channel,including a mobile telephone (i.e., smartphone).

In some embodiments, systems according to the present disclosure may beconfigured to include a communication interface. In some embodiments,the communication interface includes a receiver and/or transmitter forcommunicating with a network and/or another device. The communicationinterface can be configured for wired or wireless communication,including, but not limited to, radio frequency (RF) communication (e.g.,Radio-Frequency Identification (RFID), Zigbee communication protocols,WiFi, infrared, wireless Universal Serial Bus (USB), Ultra-Wide Band(UWB), Bluetooth® communication protocols, and cellular communication,such as code division multiple access (CDMA) or Global System for Mobilecommunications (GSM).

In one embodiment, the communication interface is configured to includeone or more communication ports, e.g., physical ports or interfaces suchas a USB port, an RS-232 port, or any other suitable electricalconnection port to allow data communication between the subject systemsand other external devices such as a computer terminal (for example, ata physician's office or in hospital environment) that is configured forsimilar complementary data communication.

In one embodiment, the communication interface is configured forinfrared communication, Bluetooth® communication, or any other suitablewireless communication protocol to enable the subject systems tocommunicate with other devices such as computer terminals and/ornetworks, communication enabled mobile telephones, personal digitalassistants, or any other communication devices which the user may use inconjunction.

In one embodiment, the communication interface is configured to providea connection for data transfer utilizing Internet Protocol (IP) througha cell phone network, Short Message Service (SMS), wireless connectionto a personal computer (PC) on a Local Area Network (LAN) which isconnected to the internet, or WiFi connection to the internet at a WiFihotspot.

In one embodiment, the subject systems are configured to wirelesslycommunicate with a server device via the communication interface, e.g.,using a common standard such as 802.11 or Bluetooth® RF protocol, or anIrDA infrared protocol. The server device may be another portabledevice, such as a smart phone, Personal Digital Assistant (PDA) ornotebook computer; or a larger device such as a desktop computer,appliance, etc. In some embodiments, the server device has a display,such as a liquid crystal display (LCD), as well as an input device, suchas buttons, a keyboard, mouse or touch-screen.

In some embodiments, the communication interface is configured toautomatically or semi-automatically communicate data stored in thesubject systems, e.g., in an optional data storage unit, with a networkor server device using one or more of the communication protocols and/ormechanisms described above.

Output controllers may include controllers for any of a variety of knowndisplay devices for presenting information to a user, whether a human ora machine, whether local or remote. If one of the display devicesprovides visual information, this information typically may be logicallyand/or physically organized as an array of picture elements. A graphicaluser interface (GUI) controller may include any of a variety of known orfuture software programs for providing graphical input and outputinterfaces between the system and a user, and for processing userinputs. The functional elements of the computer may communicate witheach other via a system bus. Some of these communications may beaccomplished in alternative embodiments using network or other types ofremote communications. The output manager may also provide informationgenerated by the processing module to a user at a remote location, e.g.,over the Internet, phone or satellite network, in accordance with knowntechniques. The presentation of data by the output manager may beimplemented in accordance with a variety of known techniques. As someexamples, data may include SQL, HTML or XML documents, email or otherfiles, or data in other forms. The data may include Internet URLaddresses so that a user may retrieve additional SQL, HTML, XML, orother documents or data from remote sources. The one or more platformspresent in the subject systems may be any type of known computerplatform or a type to be developed in the future, although theytypically will be of a class of computer commonly referred to asservers. However, they may also be a main-frame computer, a workstation, or other computer type. They may be connected via any known orfuture type of cabling or other communication system including wirelesssystems, either networked or otherwise. They may be co-located, or theymay be physically separated. Various operating systems may be employedon any of the computer platforms, possibly depending on the type and/ormake of computer platform chosen. Appropriate operating systems includeWindows 10, Windows NT®, Windows XP, Windows 7, Windows 8, iOS, SunSolaris, Linux, OS/400, Compaq Tru64 Unix, SGI IRIX, Siemens ReliantUnix, Ubuntu, Zorin OS and others.

FIG. 8 depicts a general architecture of an example computing device 800according to certain embodiments. The general architecture of thecomputing device 800 depicted in FIG. 8 includes an arrangement ofcomputer hardware and software components. The computing device 800 mayinclude many more (or fewer) elements than those shown in FIG. 8. It isnot necessary, however, that all of these generally conventionalelements be shown in order to provide an enabling disclosure. Asillustrated, the computing device 800 includes a processing unit 810, anetwork interface 820, a computer readable medium drive 830, aninput/output device interface 840, a display 850, and an input device860, all of which may communicate with one another by way of acommunication bus. The network interface 820 may provide connectivity toone or more networks or computing systems. The processing unit 810 maythus receive information and instructions from other computing systemsor services via a network. The processing unit 810 may also communicateto and from memory 870 and further provide output information for anoptional display 850 via the input/output device interface 840. Theinput/output device interface 840 may also accept input from theoptional input device 860, such as a keyboard, mouse, digital pen,microphone, touch screen, gesture recognition system, voice recognitionsystem, gamepad, accelerometer, gyroscope, or other input device.

The memory 870 may contain computer program instructions (grouped asmodules or components in some embodiments) that the processing unit 810executes in order to implement one or more embodiments. The memory 870generally includes RAM, ROM and/or other persistent, auxiliary ornon-transitory computer-readable media. The memory 870 may store anoperating system 872 that provides computer program instructions for useby the processing unit 810 in the general administration and operationof the computing device 800. The memory 870 may further include computerprogram instructions and other information for implementing aspects ofthe present disclosure.

For example, in one embodiment, the memory 870 includes a cell phenotypeidentification module 874 for identifying phenotypes of cells in thesample and a cell phenotype classification module 876 for determiningwhether a cell has a phenotype belonging to a collection ofpredetermined phenotypes and issuing an instruction for the cell sorterto sort.

In certain embodiments, the subject systems are flow cytometric systemsemploying the above described algorithm for sorting particles, such ascells, in a sample based on order of identification. Suitable flowcytometry systems may include, but are not limited to, those describedin Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford Univ.Press (1997); Jaroszeski et al. (eds.), Flow Cytometry Protocols,Methods in Molecular Biology No. 91, Humana Press (1997); Practical FlowCytometry, 3rd ed., Wiley-Liss (1995); Virgo, et al. (2012) Ann ClinBiochem. January; 49(pt 1):17-28; Linden, et. al., Semin Throm Hemost.2004 October; 30(5):502-11; Alison, et al. J Pathol, 2010 December;222(4):335-344; and Herbig, et al. (2007) Crit Rev Ther Drug CarrierSyst. 24(3):203-255; the disclosures of which are incorporated herein byreference. In certain instances, flow cytometry systems of interestinclude BD Biosciences FACSCanto™ II flow cytometer, BD Accuri™ flowcytometer, BD Biosciences FACSCelesta™ flow cytometer, BD BiosciencesFACSLyric™ flow cytometer, BD Biosciences FACSVerse™ flow cytometer, BDBiosciences FACSymphony™ flow cytometer BD Biosciences LSRFortessa™ flowcytometer, BD Biosciences LSRFortess™ X-20 flow cytometer and BDBiosciences FACSCalibur™ cell sorter, a BD Biosciences FACSCount™ cellsorter, BD Biosciences FACSLyric™ cell sorter and BD Biosciences Via™cell sorter BD Biosciences Influx™ cell sorter, BD Biosciences Jazz™cell sorter, BD Biosciences Aria™ cell sorters and BD BiosciencesFACSMelody™ cell sorter, or the like.

In some embodiments, the subject particle sorting systems are flowcytometric systems, such those described in U.S. Pat. Nos. 9,952,076;9,933,341; 9,726,527; 9,453,789; 9,200,334; 9,097,640; 9,095,494;9,092,034; 8,975,595; 8,753,573; 8,233,146; 8,140,300; 7,544,326;7,201,875; 7,129,505; 6,821,740; 6,813,017; 6,809,804; 6,372,506;5,700,692; 5,643,796; 5,627,040; 5,620,842; 5,602,039; the disclosure ofeach of which are herein incorporated by reference in their entirety.

Computer-Readable Storage Medium for Flow Cytometrically Sorting ASample with Particles, Such as Cells, Based on Order of Identification

Aspects of the present disclosure further include non-transitorycomputer readable storage mediums having instructions for practicing thesubject methods. Computer readable storage mediums may be employed onone or more computers for complete automation or partial automation of asystem for practicing methods described herein. In certain embodiments,instructions in accordance with the method described herein can be codedonto a computer-readable medium in the form of “programming,” where theterm “computer readable medium” as used herein refers to anynon-transitory storage medium that participates in providinginstructions and data to a computer for execution and processing.Examples of suitable non-transitory storage media include a floppy disk,hard disk, optical disk, magneto-optical disk, CD-ROM, CD-ft magnetictape, non-volatile memory card, ROM, DVD-ROM, Blue-ray disk, solid statedisk, and network attached storage (NAS), whether or not such devicesare internal or external to the computer. A file containing informationcan be “stored” on a computer readable medium, where “storing” meansrecording information such that it is accessible and retrievable at alater date by a computer. The computer-implemented method describedherein can be executed using programming that can be written in one ormore of any number of computer programming languages. Such languagesinclude, for example, Java (Sun Microsystems, Inc., Santa Clara,Calif.), Visual Basic (Microsoft Corp., Redmond, Wash.), and C++ (AT&TCorp., Bedminster, N.J.), as well as any many others.

In some embodiments, computer readable storage media of interest includea computer program stored thereon, where the computer program whenloaded on the computer includes instructions having: algorithm foridentifying cell phenotypes based on one or more data signals generatedfrom light detected from cells of the sample; and algorithm forinstructing a cell sorter to dynamically sort into partitions cells thathave a phenotype of a collection of predetermined phenotypes based onorder of identification.

The computer readable storage medium may include instructions forcapturing one or more images of a flow stream, such as 2 or more imagesof the flow stream, such as 3 or more images, such as 4 or more images,such as 5 or more images, such as 10 or more images, such as 15 or moreimages and including 25 or more images. In certain embodiments, thecomputer readable storage medium includes instructions for opticaladjustment of the captured images, such as to enhance the opticalresolution of the image. In certain embodiments, computer readablestorage medium may include instructions for enhancing the resolution ofthe captured images by 5% or greater, such as by 10% or greater, such asby 25% or greater, such as by 50% or greater and including enhancing theresolution of the captured images by 75% or greater.

In embodiments, computer readable storage media of interest includesalgorithm for instructing the cell sorter to stop sorting a firstphenotype from the collection of phenotypes after a predetermined numberof cells that have the first phenotype are sorted. In certainembodiments, the computer readable storage media includes algorithm forinstructing the cell sorter to dynamically sort a predetermined numberof cells into a partition. In other embodiments, computer readablestorage media includes algorithm for instructing a support stage to movea multi-well plate to a second well after a predetermined number ofcells are sorted into a first well.

The computer readable storage medium may also include algorithm forrecording the phenotypes of cells sorted into partitions. In someembodiments, computer readable storage media of interest includealgorithm for recording the phenotypes of cells sorted into partitionsby adding representations of phenotypes of sorted cells to a list ofphenotypes. In such embodiments, computer readable storage media ofinterest may include algorithm for querying the list of phenotypes todetermine whether or not a first cell has already been sorted, whereinthe first cell has a phenotype also exhibited by a second cell. In otherembodiments, computer readable storage media of interest includealgorithm for recording the phenotypes of cells sorted into partitionsby adding representations of phenotypes of sorted cells to aprobabilistic data structure. In such embodiments, computer readablestorage media of interest may include algorithm for querying theprobabilistic data structure to determine whether or not a first cellhas already been sorted, wherein the first cell has a phenotype alsoexhibited by a second cell. In some instances, the probabilistic datastructure is a Bloom filter. In other embodiments, computer readablestorage media of interest include algorithm for recording the phenotypesof cells sorted into partitions by adding representations of phenotypesof sorted cells to an associative array. In such embodiments, computerreadable storage media of interest may include algorithm for queryingthe associative array to determine whether or not a first cell hasalready been sorted, wherein the first cell has a phenotype alsoexhibited by a second cell. In some instances, the associative array isa content addressable memory. In some embodiments, computer readablestorage media of interest include algorithm for recording the phenotypesof cells sorted into partitions by setting a value at a location in atruth table corresponding to a cell phenotype. In such embodiments,computer readable storage media of interest may include algorithm forquerying the truth table to determine whether or not a first cell hasalready been sorted, wherein the first cell has a phenotype alsoexhibited by a second cell. In other embodiments, computer readablestorage media of interest include algorithm for recording informationidentifying a partition and a phenotype of a cell sorted into thepartition. In such embodiments, computer readable storage media ofinterest may include algorithm for maintaining a list identifyingpartitions and phenotypes of cells sorted into the partitions.

The non-transitory computer readable storage medium may also includealgorithm for calculating one or more parameters of the particle. Inthese embodiments, the computer readable storage medium includesalgorithm for instructing the cell sorter to: dynamically sort into afirst partition cells of the sample that exhibit a first collection ofphenotypes, comprising one or more phenotypes, based on order ofidentification, and dynamically sort into a second partition cells ofthe sample that do not exhibit the first collection of phenotypes. Insuch embodiments, computer readable storage media of interest mayinclude algorithm for dynamically updating the first collection ofphenotypes based on sorted cells in the sample so that cells sorted intothe first partition and cells sorted into the second partition comprisesubstantially the same number of cells.

The non-transitory computer readable storage medium may also includealgorithm for not sorting a cell exhibiting an indeterminate phenotype.

The non-transitory computer readable storage medium may also includealgorithm for calculating an estimate of an amount of fluorescentspillover measured with respect to a single data signal based on apredicted variance-covariance matrix of unmixed data for a sorted cell.In such embodiments, computer readable storage media of interest mayinclude algorithm for calculating an estimate of a covariance betweentwo fluorophores based on the predicted variance-covariance matrix ofunmixed data for the sorted cell. In such embodiments, computer readablestorage media of interest may include algorithm for calculatingthresholds for expression for a fluorophore based on the estimatedcovariance between fluorophores. In such embodiments, computer readablestorage media of interest may include algorithm for calculating ameasure of uncertainty with respect to the phenotype exhibited by asorted cell based on the estimated covariance between fluorophores.

The computer readable storage medium may be employed on one or morecomputer systems having a display and operator input device. Operatorinput devices may, for example, be a keyboard, mouse, or the like. Theprocessing module includes a processor which has access to a memoryhaving instructions stored thereon for performing the steps of thesubject methods. The processing module may include an operating system,a graphical user interface (GUI) controller, a system memory, memorystorage devices, and input-output controllers, cache memory, a databackup unit, and many other devices. The processor may be a commerciallyavailable processor, or it may be one of other processors that are orwill become available. The processor executes the operating system andthe operating system interfaces with firmware and hardware in awell-known manner, and facilitates the processor in coordinating andexecuting the functions of various computer programs that may be writtenin a variety of programming languages, such as Java, Perl, C++, otherhigh level or low level languages, as well as combinations thereof, asis known in the art. The operating system, typically in cooperation withthe processor, coordinates and executes functions of the othercomponents of the computer. The operating system also providesscheduling, input-output control, file and data management, memorymanagement, and communication control and related services, all inaccordance with known techniques.

Utility

The subject systems, methods and computer systems find use in a varietyof applications where it is desirable to analyze and sort particlecomponents, such as cells, in a sample in a fluid medium, such as abiological sample. In some embodiments, the systems and methodsdescribed herein find use in flow cytometry characterization ofbiological samples labeled with fluorescent tags. In other embodiments,the systems and methods find use in spectroscopy of emitted light. Inaddition, the subject systems and methods find use in improving theefficiency of sorting a sample (e.g., in a flow stream). By improvingthe efficiency of sorting a sample, it is meant that fewer particles,such as cells, of a sample may be wasted (i.e., disposing of particlessuch as cells such that they go unused) when sorting a sample when thesubject systems and methods are employed. In particular, the subjectsystems and methods may improve efficiency of sorting and in particularreduce the number of cells wasted when cells with relatively lowfrequency phenotypes within a sample are sorted. In certain instances,the efficiency of sorting may be improved such that more variations ofparticles may be collected and sorted when the subject systems andmethods are employed. By variations of particles, it is meant, forexample, cell phenotypes, such that a larger number of different cellphenotypes are sorted when embodiments of the present disclosure areemployed. Embodiments of the present disclosure find use where it isdesirable to provide a flow cytometer with improved cell sortingefficiency, enhanced particle collection, particle charging efficiency,more accurate particle charging and enhanced particle deflection duringcell sorting.

Embodiments of the present disclosure also find use in applicationswhere cells prepared from a biological sample may be desired forresearch, laboratory testing or for use in therapy. In some embodiments,the subject methods and devices may facilitate obtaining individualcells prepared from a target fluidic or tissue biological sample. Forexample, the subject methods and systems facilitate obtaining cells fromfluidic or tissue samples to be used as a research or diagnosticspecimen for diseases such as cancer. Likewise, the subject methods andsystems may facilitate obtaining cells from fluidic or tissue samples tobe used in therapy. Methods and devices of the present disclosure allowfor separating and collecting cells from a biological sample (e.g.,organ, tissue, tissue fragment, fluid) with enhanced efficiency and lowcost as compared to traditional flow cytometry systems.

Although the foregoing invention has been described in some detail byway of illustration and example for purposes of clarity ofunderstanding, it is readily apparent to those of ordinary skill in theart in light of the teachings of this invention that certain changes andmodifications may be made thereto without departing from the spirit orscope of the appended claims.

Accordingly, the preceding merely illustrates the principles of theinvention. It will be appreciated that those skilled in the art will beable to devise various arrangements which, although not explicitlydescribed or shown herein, embody the principles of the invention andare included within its spirit and scope. Furthermore, all examples andconditional language recited herein are principally intended to aid thereader in understanding the principles of the invention and the conceptscontributed by the inventors to furthering the art and are to beconstrued as being without limitation to such specifically recitedexamples and conditions. Moreover, all statements herein recitingprinciples, aspects, and embodiments of the invention as well asspecific examples thereof, are intended to encompass both structural andfunctional equivalents thereof. Additionally, it is intended that suchequivalents include both currently known equivalents and equivalentsdeveloped in the future, i.e., any elements developed that perform thesame function, regardless of structure. Moreover, nothing disclosedherein is intended to be dedicated to the public regardless of whethersuch disclosure is explicitly recited in the claims.

The scope of the present invention, therefore, is not intended to belimited to the exemplary embodiments shown and described herein. Rather,the scope and spirit of present invention is embodied by the appendedclaims. In the claims, 35 U.S.C. § 112(f) or 35 U.S.C. § 112(6) isexpressly defined as being invoked for a limitation in the claim onlywhen the exact phrase “means for” or the exact phrase “step for” isrecited at the beginning of such limitation in the claim; if such exactphrase is not used in a limitation in the claim, then 35 U.S.C. § 112(f)or 35 U.S.C. § 112(6) is not invoked.

1. A method of flow cytometrically sorting a sample, the methodcomprising: introducing the sample into a flow cytometer; flowing theintroduced sample in a flow stream; irradiating the sample in the flowstream with a light source; detecting light from cells in the sampleflowing in the flow stream; identifying phenotypes of cells in thesample flowing in the flow stream based on one or more data signalsgenerated from the detected light; and dynamically sorting intopartitions cells of the sample that have a phenotype of a collection ofpredetermined phenotypes based on order of identification.
 2. The methodaccording to claim 1, further comprising excluding a first phenotypefrom the collection of phenotypes after sorting a predetermined numberof cells that have the first phenotype.
 3. The method according to claim1, wherein a predetermined number of cells are sorted into a partition.4. The method according to claim 1, wherein the partitions comprisewells.
 5. The method according to claim 4, wherein the wells are wellsof a multi-well plate.
 6. The method according to claim 5, furthercomprising advancing the multi-well plate to a second well after sortinga predetermined number of cells into a first well.
 7. The methodaccording to claim 1, further comprising recording the phenotypes ofcells sorted into partitions. 8-18. (canceled)
 19. The method accordingto claim 1, further comprising recording information identifying apartition and a phenotype of a cell sorted into the partition. 20.(canceled)
 21. The method according to claim 1, further comprisingestimating, based on a subset of cells in the sample, a number ofphenotypes exhibited by cells in the sample.
 22. The method according toclaim 1, wherein sorting cells comprises deflecting cells into a firstpartition and a second partition. 23-26. (canceled)
 27. The methodaccording to claim 1, wherein identifying cell phenotypes is based onone or more values of parameters that are determined from the datasignals generated from the detected light.
 28. The method according toclaim 27, wherein certain values of a parameter of a cell indicate anindeterminate state of whether the cell has a phenotype.
 29. The methodaccording to claim 28, wherein a cell is not sorted when it exhibits anindeterminate state of whether the cell has a phenotype.
 30. The methodaccording to claim 1, further comprising estimating an amount offluorescent spillover measured with respect to a single data signalbased on a predicted variance-covariance matrix of unmixed data for asorted cell.
 31. The method according to claim 30, further comprisingestimating a covariance between two fluorophores based on the predictedvariance-covariance matrix of unmixed data for the sorted cell.
 32. Themethod according to claim 31, further comprising defining thresholds forexpression for a fluorophore based on the estimated covariance betweenfluorophores.
 33. The method according to claim 32, further comprisingdefining a measure of uncertainty with respect to the phenotypeexhibited by a sorted cell based on the estimated covariance betweenfluorophores.
 34. (canceled)
 35. The method according to claim 1,wherein the collection of predetermined phenotypes comprises a cell typewith one or more cell subtypes. 36-48. (canceled)
 49. The methodaccording to claim 1, wherein the flow stream is irradiated with a lightsource at a wavelength from 200 nm to 800 nm.
 50. The method accordingto claim 49, wherein the method comprises irradiating the flow streamwith a first beam of frequency shifted light and second beam offrequency shifted light. 51-122. (canceled)